presentation layer vs semantic layer

The Rise of the Semantic Layer: Metrics On-The-Fly

What Tools Are Out There for Building a Semantic Layer?

As we’ve now learned a lot about the semantic layer, let's see actual tools, focusing on open-source.

The most commonly named tools are Cube.js (recently renamed Cube), MetricFlow , MetriQL , dbt Metrics , or Malloy . Where MetriQL was the first open-source but Transform.co working on a closed-source version that eventually got open-sourced into MetricFlow. Also, dbt announced their metrics system back at Coalesce the Metric System , and turning it now into dbt Semantic Layer (more to come in October at dbt Coalesce Conference). According to GitHub stars, Cube is the fastest growing tool in this area, and already has many integrations including data sources and data visualizations – for example, it integrates with dbt Metrics.

MetricFlow and Cube’s semantic layer overview is fascinating to compare, where Cube talks about headless BI and MetricFlow about the metrics layer. As discussed above, you understand why both try to address not only the metrics part but also data modeling and abstract away all data sources by adding access control, caching, and APIs.

Overview of a Semantic Layer by Cube (top) and MetricFlow (bottom)

‍ When do you need such a tool? There are two initial use cases: First, if you won’t build a significant data architecture, you could start with your sources and access your data sources or cloud warehouse. The second one is when your company size and data-savvy people are growing. More people need transformations, defining metrics, or adding many data and SaaS tools ; you need alignment around definitions.

Some trends : Supergrain initially also had a metrics layer but pivoted to personalized marketing. If we look solely at the GitHub stars, it is a bit unfair for MetricFlow as they worked to log on that problem but only open-sourced it, but still, it gives a good impression about cube.js. They also just announced the Unbundling of Looker in combination with Superset. Furthermore, there is a dedicated Semantic Layer Summit .

Open-source semantic layer solutions compared on GitHub Star History

A deeper comparison of the tools you find on How is this different from . There is an extensive list of closed-source solutions such as uMetric by Uber, Minerva by Airbnb, Veezoo , and more.

🐦 Unpopular Opinion: The Metrics Stores are just BI Tools that evolve into full-fledged BI tools over time [ Tweet ] Nick Hande, Co-founder of Transform, replied: “ I think I actually agree with this. But, I don’t think BI Tools will exist as they do today. Instead, there will be data applications that will rely on semantics expressed in Metrics Stores. The Metrics Store will need governance + discovery that includes graphs(“BI”) + metadata” .

💬 An interesting announcement about the Customer Data Platform (CDP) , a relatively new term, and companies already pivoting away from it. Hightouch talks about “legacy CPD”. Fascinating to me, because the new data architecture looks like a semantic layer on top of the data warehouse, or is it just me?

Semantic Layer Challenges

Besides all the positive things mentioned, some challenges come with a semantic layer. First of all, it is another layer that needs integration with many other tools. Another significant downside is having to learn another solution that needs to be operationalized as a critical component of your data stack. The cost of maintaining and creating such a layer is high.

There is also a hidden complexity in generating these queries on the fly. Every query needs to be generated for different SQL dialects–e.g., Postgres SQL and Oracle SQL are not 1:1 the same. That can be both problematic in terms of latency but also in terms of producing a faulty SQL query . The counter-argument is that it’s still easier to maintain copies of data sets and re-do the metrics repetitively inside a BI dashboard.

When you use extensive exposed APIs, you might run into performance issues, as pulling lots of data over REST or GraphQL is less than ideal. You can always switch to SQL directly, but mostly with losing some comfort.

As always, it depends on the criticality of centrally defined metrics that everyone agrees on, or you can live in minor drift away in different tools.

🗣 What others are saying: JP Monteiro is saying in his deep dive : “ I find it unlikely that the best practice will still be to have one place to define metrics and one place to define dimensions, once the querying layer part is solved. In fact, lineage between concepts is part of “semantics”: it helps us understand how one concept is related to another —which makes you really question if ‘column-level lineage’ is the right level of abstraction we should talk about: columns are too raw ".

Semantic layer vs data mart vs presentation layer

Semantic Layer: It serves as a connector between sources of raw data and the presentation layer, providing a collective view of the data and abstracting away the complexities of underlying data structures.
Data Mart: It is a subset of a data warehouse designed to serve a specific department or line of business, containing a tailored set of data optimized for analysis and reporting.
Presentation Layer: In this layer the data is visualized and presented to the end-users, often in the form of dashboards, reports, or applications, making it understandable and actionable.
Semantic Layer: This layer includes different business logic, data definitions, and relationships between various data elements, enabling users to query and analyze data with the help of familiar business terms rather than database terminology.
Data Mart: It contains structured data relevant to a particular business function or area, such as sales, marketing, or finance, organized for analytical purposes.
Presentation Layer: It has a visual representation of data, which includes charts, graphs, tables, and interactive elements, designed to facilitate decision-making and insight generation.
Semantic Layer: It is primarily designed for data analysts, business intelligence professionals, and other technical users who need to access and analyze data across different sources in a consistent manner.
Data Mart: It serves business users and analysts within a specific department or business unit, providing them with relevant and tailored data for their analytical needs. ‍
Presentation Layer: It caters to a broader audience, including executives, managers, and operational staff, who rely on intuitive and informative visualizations to understand trends, identify patterns, and make informed decisions.

What’s the Difference to OLAP, Data Cataloging, Virtualizations, or Mesh

As the semantic layer is something central and intertwined with lots of related data engineering concepts , we discuss here how they relate to each other.

As part of the semantic layer, the cache layer can be seen as a replacement for modern OLAP cubes such as Apache Druid , Apache Pinot , and ClickHouse . Similar attributes with defining the queries ad-hoc and delivering sub-second query times. OLAP cubes are the fastest way to query your data if you do not have updates in your data.

Compared to an OLAP solution, the benefit of the semantic layer is avoiding reingesting your data into another tool and format; it happens under the hood, for good or worse. A caching layer is another complex piece, as it's always outdated as you add more data; it needs to constantly update its cache as you do not want to query old invalidated data.

That's also where it gets interesting how semantic layer tools solve it. For example, Cube tried to implement with Redis but reverted and built their own caching layer from scratch. On the upside, OLAP cubes have built-in computing where you can run heavy queries .

Data Virtualization and Federation

Data Virtualization comes up in many discussions related to the semantic layer. Still, even more to the semantic layer is data virtualization, with tools such as Dremio that try to have all data in-memory with technologies such as Apache Arrow . Data federation, very similar to virtualization, mostly referred to technologies like Presto or Trino .

These are versions of a semantic layer, including a cache layer with access management, data governance, and many more. It has a powerful option to join data in its semantic layer . In a way, they are the perfect semantic layer, although Dremio, and also Presto have branded themself as an open data lakehouse platform lately. Which brings us to another question.

Is a lakehouse nothing else than a semantic layer?

In a way, they have similar attributes, but a lakehouse includes an open storage layer ( Delta Lake, Iceberg, Hudi ). In contrast, the semantic layer is more ad-hoc, query-time driven. Still, the lakehouse from Databricks tries to store only once and avoid data movement as much as possible by querying it with their compute engine Photon .

The problem is, the platform itself is not open-source, only storage, and the metrics are not defined in a declarative way. They are mostly entangled in notebooks or UI-based tools.

📝 Dremio has patented database-like indexes on source systems with Data Reflections . They are producing more cost-effective query plans than performing query push-downs to the data sources.

Data Mesh and Data Contracts

Two recent popular terms are Data Mesh and Data Contract . Both are pulling in the same direction of giving more power to the domain experts who know the data best, away from engineers who should be more focused on a stable system.

In my opinion, giving more control also needs an easier way for these domain experts to define their metrics in a standardized way, being declarative directly on the Data Assets , but with a standardized tool—which is the semantic layer for me. But in contrast to data mesh, which is decentralized, the semantic layer fancies a very centralized approach for metrics.

Data Catalogs

A data catalog is another way of centralizing metrics and Data Products . It’s similar to a semantic layer but focuses more on the physical data assets than the metrics query. Plus, it has another focus: providing a Google search to your data assets such as dashboards, warehouse tables, or models. Modern tools such as Amundsen or DataHub allow you to rate and comment on the data assets, adding metadata such as an owner so that people easily find the best data collaboratively set for their job.

The Semantic Warehouse

A Semantic Warehouse is a term I heard from Chad Sanderson for the first time a month ago. He does a great job putting the words semantic layer, semantic mapping, metrics layer, and data catalog on a data map, as seen in the image below.

An overview of Semantic Warehouse by Chad Sanderson

What’s interesting here is that the semantic layer is directly on top of the apps, services, and DB between semantic mappings and the metrics layer. It acts as a Data Contract between the real world and the data team. In my opinion, this is what a semantic layer is described in great detail above.

Analytics API

In the above semantic warehouse illustration, the semantic mapping seems to be the data orchestrator , and the metrics layer is the subset of the semantic layer that holds the metrics themselves. This is interesting because I implemented such a thing (parts of it), and I wrote about it in a similar way in Building an Analytics API with GraphQL . I called it Analytics API with its core components of an API and query engine, data catalog, data orchestrator, a SQL connector, and of course, the metrics layer. It has the same function and the same component but is visualized as a single analytics API component. Queries through the access layer connecting heterogeneous data stores.

Suggested read: Data Catalog vs. Data Discovery

Will the Semantic Layer Get More Adoption?

The semantic layer is an abstract construct that is hard to grasp. It has many touching points with existing concepts and similarities with other upcoming ones.

If it were a new construct, I'd say it's just another buzzword. But as it started in 1991, as seen in the history of the semantic layer, and evolved into the modern data stack and adapted to today's needs.

It's still hard to implement all features we've discussed here; that's the theoretical view. But I believe if you start with getting a more diverse architecture with lots of spread-out tools, the semantic layer has its stands for staying. Start with your basic requirements. Maybe you need a central access layer for people to access data quickly. Or you do not want to add another complex layer with an OLAP cube on top and search for an efficient cache layer. Or most importantly, if you're going to define your metric in a central place with a thin layer, start with the concept of the semantic layer and its definition of metrics.

Learn along the way. Check out the above tools mentioned and see if they work out. These days, the tech makes it very easy to start a POC, e.g., define some of your BI metrics inside a semantic layer and sync it into your BI tool. Try to get a feeling for it.

Besides the must needs such as data integration, transformation at ingest, visualizing, and orchestrating, I see the semantic layer as the next step for defining metrics in a standardized way.

If you want to read more, I pulled together an extensive list of other articles on that topic in our data glossary on What is a Semantic Layer .

If you are curious about how we Build the Data Stack and integrate a semantic layer at Airbyte and stay up to date with our Newsletter , we plan to share a hands-on tutorial about it. Or, if you want to chat with 9000+ data people and us, join our Community Slack .

How to build a semantic layer?

Before constructing the semantic layer, analyze the data sources and business requirements. Next, determine the definitions, relationships, and important data entities. Then, create a logical data model that satisfies business requirements while abstracting technical complexities. Utilize a business intelligence platform or a data modeling tool to put the model into practice. Finally, ensure that the semantic layer provides precise, consistent, and useful insights by iteratively validating and improving it based on feedback from stakeholders.

How do different business applications use the semantic layer?

Semantic layers serve multiple purposes across various domains within the realm of data management and analytics. In business intelligence (BI) tools, they provide users with a unified perspective of data, streamlining reporting and querying processes. Data virtualization tools leverage semantic layers to seamlessly integrate disparate data sources, thereby enhancing data agility and accessibility. Data analysis tools utilize semantic layers to enhance analytical capabilities and comprehend intricate data relationships. Moreover, within data governance systems, semantic layers play a pivotal role in enforcing standardized data definitions, access restrictions, and compliance guidelines throughout the organization.

How do semantic layers help LLMs better interpret data?

Semantic layers significantly enhance the interpretative capabilities of Large Language Models (LLMs) by providing a structured, contextual framework for data analysis. By abstracting complex data structures and technical specifics, semantic layers enable LLMs to access and analyze data uniformly, thus improving their ability to understand intricate relationships and contextual nuances. This unified approach allows LLMs to deliver more precise and insightful analyses, making them invaluable tools in decision-making processes across various domains. As LLMs apply these layers, they better identify trends, contextualize information, and generate accurate insights, supporting users in navigating and making informed decisions in complex environments. This refined processing aids in bridging the gap between vast data sets and practical, actionable intelligence.

About the Author

Simon is a Data Engineer and Technical Author at Airbyte . He is dedicated, empathetic, and entrepreneurial with 15+ years of experience in the data ecosystem. He enjoys maintaining awareness of new innovative and emerging open-source technologies.

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Semantic Layers 101: Everything You Need to Know to Get Started

As organizations grow and data becomes more complex, maintaining consistency and accessibility across various data sources can be a significant challenge. A semantic layer addresses this by providing a unified, business-friendly view of data that abstracts the underlying complexity. By implementing a semantic layer, companies can ensure consistency in metrics, reduce the reliance on technical teams for report generation, and empower non-technical users to make data-driven decisions.

At Select Star, we see our customers leveraging semantic layers to abstract away the complexities of underlying data models and present information in business-friendly terms. This enables business users to be able to understand and use the metrics without worrying about the underlying data complexity. In addition, having a semantic layer facilitates data governance by enforcing consistent data definitions and ensuring compliance with regulatory requirements.

This guide will explore the ins and outs of semantic layers, their importance, and how to implement them effectively in your organization.

What is a Semantic Layer?

At its core, it's an intermediary that sits between data sources and analytics tools, translating technical database schemas into concepts and language that business users can easily understand and work with. Unlike traditional data models that focus primarily on structure, a semantic layer provides a business-oriented view of data. They incorporate several dimensions, including business glossaries, data models, measures, calculations, dimensions, relationships, and relationships. These work together to create a unified data language that both technical and business users can use to unlock the full context of their data and leverage it in their work.

The heart of a semantic layer lies in its ability to define and standardize key business concepts. For instance, a semantic layer might include a precise definition of "revenue" that accounts for various business rules and calculations, ensuring that this metric is consistently understood and applied across the organization. Similarly, it could define customer segments or product categories in a way that aligns with business objectives, making these dimensions readily available for analysis without requiring users to understand the underlying data structures.

Several types of semantic layers exist, each tailored to specific use cases and environments. Universal semantic layers provide a single, consistent interface across multiple data sources and analytics tools. Data warehouse-specific layers optimize performance for structured, centralized data repositories. Data lake semantic layers bring order and meaning to vast pools of raw, unstructured information. BI-specific semantic layers are tightly integrated with particular business intelligence platforms, streamlining report creation and analysis within those tools.

Regardless of the specific implementation, all effective semantic layers share core components and functionalities. These include the following 3 main components:

A robust semantic data model that defines entities, attributes, and relationships in business terms
Measures and calculations that are performed on columns of the model and capture complex business logic
Dimensions and relationships that provide context and enable multifaceted analysis, including grouping and filtering

Why implement a Semantic Layer?

Implementing a semantic layer offers numerous benefits that can transform how organizations interact with and derive value from their data. We outline four key reasons below: unified data language, improved data governance, enhanced self-service analytics, and faster time-to-insight.

‍ Unified data language: By providing a single, authoritative source of definitions for business metrics and dimensions, semantic layers eliminate inconsistencies that often arise when different teams interpret data in their own ways. This unified language fosters better communication and alignment across departments. ‍
Improved data governance: Semantic layers centralize data definitions and access controls, making it easier to enforce data governance policies and ensure compliance with regulatory requirements. They provide a clear audit trail of how data is being used and interpreted across the organization.
‍ Enhanced self-service analytics: With a well-designed semantic layer, business users can explore data and create reports without needing to understand complex database schemas or write SQL queries. This democratization of data access empowers users to answer their own questions quickly, reducing the burden on engineering and data teams.
‍ Faster time-to-insight: By abstracting away the complexities of data integration and transformation, semantic layers enable business users to focus on analysis rather than data preparation. This acceleration of the analytical process leads to faster, more data-driven decision-making.

What to look for in a Semantic Layer tool?

Some popular semantic layer tools or tools with semantic layer capabilities include dbt, Cube.js, AtScale, Tableau, and PowerBI. When evaluating semantic layer tools, several key factors should be considered, including integration capabilities, metadata management, data modeling flexibility, and performance optimization. With sitting between the querying and presentation layer, it should be no surprise to expect the tool to seamless connect to your existing data sources, querying mechanisms, and BI tools. Robust metadata management, such as support for data dictionaries and glossaries, goes hand in hand with integration capabilities so that creation of semantic data models can be automated and this context in the tool can be shared across other metadata repositories in their organizations data ecosystem. Data modeling should be flexible enough to support your most advanced metric. Also consider one that supports both visual and code-based modeling to accommodate different user preferences. Performance optimization features like intelligent caching and query optimization features will ensure responsiveness at scale.

How to design an effective Semantic Layer?

Creating a robust semantic layer requires careful planning and collaboration between technical and business stakeholders. Here's a step-by-step approach to designing an effective semantic layer:

‍ Start with Business Requirements : Begin by identifying the critical metrics and dimensions that drive your business. Engage with key stakeholders to understand their analytical needs and priorities. This business-first approach ensures that your semantic layer addresses real organizational needs rather than simply mirroring existing data structures.
‍ Design the Semantic Model : Based on the identified requirements, create a conceptual model that represents business entities, their relationships, and key metrics. This model should bridge the gap between technical data structures and business concepts, using terminology that resonates with business users.
‍ Integrate with Data Sources : Map your semantic model to the underlying data sources, whether they're data warehouses, lakes, or operational systems. This step may involve creating views, defining transformations, or setting up automated data pipelines to ensure that the semantic layer always reflects the most up-to-date data.
‍ Test and Validate : Rigorously test your semantic layer to ensure that it produces accurate results and performs well under various query scenarios. Involve business users in this process to validate that the layer truly meets their needs and expectations.
‍ Iterate and Refine : Semantic layer design is an iterative process. As business needs evolve and new data sources become available, continuously refine and expand your semantic layer to keep it relevant and valuable.

What are the top challenges with Semantic Layers?

Despite its benefits, implementing a semantic layer comes with its own set of challenges: buy-in, implementation, and maintenance. Gaining cross-functional buy-in and ensuring adoption across the organization can be difficult, particularly in larger enterprises with established data practices. Technical implementation hurdles, such as balancing centralization with flexibility, also need to be carefully navigated. Ensuring performance at scale, particularly for complex calculations or large datasets, requires careful optimization. Keeping the semantic layer up-to-date as business definitions change and new data sources are introduced requires ongoing effort and commitment.

Looking Ahead: The Future of Semantic Layers

As data ecosystems continue to evolve, semantic layers are poised to play an even more critical role in data management and analytics, such as with active metadata, data governance, composable data systems, and AI.

Leverage Active Metadata: The next phase of semantic layers will likely incorporate active metadata, which includes information about how users interact with data. This evolution towards more dynamic, usage-aware semantic layers could significantly enhance their ability to support data-driven decision making by providing deeper insights into how data is actually being used across the organization.

Expanded Role in Governance: Semantic layers will increasingly serve as the cornerstone of data governance initiatives, providing a centralized platform for managing data definitions, lineage, and access controls.

Improved Composability: Future semantic layers will offer greater flexibility in combining and reusing semantic components across different business contexts, enabling more agile and adaptable data architectures.

AI and Machine Learning Integration: As large language models become more sophisticated, they could potentially leverage semantic layers to gain a deeper understanding of organizational data and context. This synergy could lead to more accurate, context-aware AI-driven insights and recommendations.

Semantic layers represent a powerful tool for organizations looking to bridge the gap between raw data and business value. By providing a unified, business-oriented view of data, they enable faster, more consistent, and more insightful analytics across the enterprise. With a well-designed semantic layer as your foundation, your organization will be well-positioned to turn your data assets into a true competitive advantage.

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Semantic vs Presentation Layer in Lightdash - August 2024

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Defining Semantic Layer and Presentation Layer

Key differences between semantic and presentation layers, the role of the semantic layer in data abstraction, the importance of the presentation layer in user interface design, interplay between semantic and presentation layers in application development, case studies: semantic layer vs presentation layer in action, best practices for implementing semantic and presentation layers, future trends: evolving roles of semantic and presentation layers.

Explore the distinctions between semantic and presentation layers in the context of Lightdash BI.

The distinction between the semantic layer and the presentation layer is crucial in data architecture, particularly when leveraging tools like dbt and Lightdash. Here's an in-depth look at each layer and their roles in data analysis:

Semantic Layer

Purpose : Acts as an abstraction layer that defines business logic and data models.
Benefits : Simplifies complex data into understandable business terms.
Usage : Utilized by data teams to create metrics and dimensions that are consistent across different tools.

Presentation Layer

Purpose : Focuses on the visualization and interaction with the data.
Benefits : Provides a user-friendly interface for data consumers to explore and visualize data.
Usage : Tools like Lightdash offer dynamic visualization capabilities, allowing users to select metrics and dimensions for generating charts and graphs.

When integrating the dbt Semantic Layer with Lightdash, users can leverage the semantic layer's pre-defined metrics in real-time, ensuring consistency and accuracy in reporting. Lightdash's UI dynamically matches metrics and dimensions, streamlines the visualization process, and offers customization options for the presentation layer.

Example Integration :

This approach empowers data consumers to self-serve and answer their own data questions, reducing dependency on data teams and fostering a data-driven culture within the organization.

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Simplifying Data Access

Abstracts Complexity : Hides the underlying data schema and presents a user-friendly model.
Consistent Definitions : Ensures metrics and dimensions have uniform definitions across the organization.

Enhancing Data Discovery

Discoverability : Users can easily find and understand data without deep technical knowledge.
Self-Service Analytics : Empowers users to create reports and visualizations without relying on IT.

Bridging the Gap Between Data and Business

Business Context : Aligns data with business concepts and objectives.
Collaboration : Facilitates a common language for data discussions among diverse teams.

Semantic Layer vs Presentation Layer

While the semantic layer focuses on the meaning and organization of data, the presentation layer is concerned with how data is displayed to the user. Both layers are crucial for delivering a comprehensive data experience.

Real-World Application: Lightdash and dbt Semantic Layer

Lightdash's integration with dbt Semantic Layer exemplifies the semantic layer's utility. Users can query metrics and dimensions directly through the UI, with Lightdash handling the complexities of data relationships and visualization.

By defining metrics and dimensions within the dbt model, users can leverage these pre-built blocks to explore data and gain insights, all while maintaining consistency and accuracy.

The presentation layer plays a crucial role in user interface (UI) design, serving as the bridge between users and the underlying semantic layer of an application. It is responsible for the visual representation of data and functionality, and its design directly impacts user experience (UX).

Key Aspects of the Presentation Layer

Visual Aesthetics : The look and feel of the UI, including color schemes, typography, and layout, which should align with brand identity.
Usability : The ease with which users can navigate and interact with the UI, including intuitive interfaces and clear calls to action.
Accessibility : Ensuring that the UI is usable by people with a wide range of abilities, following guidelines such as WCAG.
Responsiveness : The UI's ability to adapt to various screen sizes and devices, maintaining functionality and aesthetics.
Performance : The speed and efficiency with which the UI loads and responds to user interactions.

Incorporating Visual Aids

Visual aids such as icons, infographics, and animations can enhance comprehension and engagement. Code snippets for common UI elements can be provided to streamline development.

While the semantic layer focuses on the meaning and structure of data, the presentation layer is concerned with how that data is displayed to the user. A well-designed presentation layer considers the semantic layer's output to create a meaningful and effective UI.

By adhering to these principles and utilizing official documentation for specific insights, designers can create a presentation layer that not only looks good but also provides a seamless and intuitive user experience.

Explore a detailed comparison of Lightdash and Metabase for data analytics and BI solutions.

Understanding the distinction and connection between the semantic layer and the presentation layer is crucial in application development. The semantic layer serves as an abstraction layer that defines the business logic and data models, while the presentation layer is responsible for the user interface and the display of information to the end-user.

Semantic Layer:

Encapsulates the business logic.
Provides a consistent and unified view of data across the system.
Facilitates easier data management and scalability.

Presentation Layer:

Focuses on user experience and interaction.
Implements the visual aspects of an application.
Adapts to various devices and screen sizes.

Integrating Semantic and Presentation Layers

To achieve a seamless user experience, the integration of the semantic layer with the presentation layer must be done thoughtfully. Here are some strategies:

Data Binding: Utilize frameworks that support data binding to keep the UI in sync with the underlying data model.
APIs: Design APIs that provide a clear contract between the semantic layer and the presentation layer.
Modularity: Keep the layers modular to allow for independent development and testing.

Case Study: Lightdash and dbt Semantic Layer

Lightdash's integration with the dbt Semantic Layer exemplifies the effective interplay between semantic and presentation layers. By leveraging dbt metrics, Lightdash ensures real-time data consistency across tools. Features like dynamic metric and dimension matching enhance the user experience by simplifying data exploration.

The interplay between the semantic and presentation layers is a balancing act that requires careful consideration. By understanding their roles and effectively integrating them, developers can create robust, scalable, and user-friendly applications.

In the realm of data analytics, the distinction between the semantic layer and the presentation layer is pivotal for efficient data exploration and visualization. Here's an in-depth look at how these layers function and their impact on data-driven decision-making.

Definition : A semantic layer abstracts complex data into business terms.
Functionality : It allows users to interact with data using common business vocabulary without needing to understand the underlying data structure or write complex queries.
Example : With dbt's Semantic Layer, metrics and dimensions are pre-defined, enabling users to select and query data seamlessly within tools like Lightdash.
Definition : The presentation layer is where data is visualized and presented to the user.
Functionality : It involves the creation of dashboards, charts, and reports that make data insights accessible and actionable.
Example : Lightdash leverages the dbt Semantic Layer to automatically generate visualizations, offering a library of options for customization.

Comparative Case Study

Semantic Layer Advantages : Ensures consistency in metrics across different tools, simplifies the querying process, and reduces the need for SQL expertise.
Presentation Layer Advantages : Provides a user-friendly interface for data interaction, allows for personalized data stories, and supports a wide range of visual aids to enhance data comprehension.

By integrating the semantic layer with the presentation layer, tools like Lightdash offer a comprehensive solution for data teams and business users alike, streamlining the data analysis process and fostering a data-driven culture.

Explore the differences between dbt Core and dbt Cloud within the Lightdash ecosystem. Understand which fits your data workflow.

When implementing semantic and presentation layers, it's crucial to understand the distinction and interplay between them. The semantic layer serves as an abstraction layer that simplifies complex data into business terms, while the presentation layer is where data visualization and user interaction occur. Here are some best practices:

Define Clear Objectives : Establish what you aim to achieve with your semantic layer, such as consistent metrics across tools.
Use Standardized Naming Conventions : Ensure that business terms are uniformly applied to avoid confusion.
Leverage Documentation : Utilize official documentation to align with best practices and gain unique insights.
Optimize for Performance : Consider query performance and caching strategies to enhance user experience.
Ensure Security : Implement appropriate security measures to protect sensitive data.
Facilitate Collaboration : Encourage feedback from end-users to refine the semantic layer.
Integrate with Tools : Seamlessly connect the semantic layer with visualization tools like Lightdash.

For example, integrating Lightdash with your dbt Semantic Layer allows real-time consumption of dbt metrics, ensuring accurate and consistent data representation. Remember to dynamically show matching metrics and dimensions based on the current selection and group dimensions by dbt entities for better organization.

As the digital landscape continues to evolve, the distinction between semantic and presentation layers becomes increasingly significant. Here's an in-depth look at the future trends affecting these layers:

Semantic Layer : This layer serves as an abstraction that provides meaningful context to data, making it understandable and usable by business users. It includes metadata, business logic, and data models.
Presentation Layer : This layer is responsible for the visual representation of data. It includes the design of dashboards, reports, and charts that users interact with.
Integration with Tools : Tools like Lightdash are integrating with dbt Semantic Layer to offer real-time consumption of dbt metrics, enhancing data accessibility for users.
User-Friendly Interfaces : The focus is on providing end-users with meaningful building blocks, such as pre-defined metrics and dimensions, to facilitate self-service analytics.
Dynamic Visualization : Automatic generation of visualizations based on selected metrics and dimensions is becoming a standard feature, as seen in Lightdash's capabilities.

Unique Insights

Real-Time Metrics : With dbt Semantic Layer integration, users can ensure the metrics they see are consistent across all semantic layer-connected tools.
Feedback-Driven Development : Platforms are actively seeking user feedback to improve features and address bugs, as evidenced by Lightdash's community engagement.

Organizational Impact

Self-Service Analytics : By empowering users to answer their own data questions, organizations can foster a data-driven culture.
Reduced Dependency : With pre-defined metrics, the need for SQL expertise is reduced, allowing a broader range of users to engage with data.

Future Developments

Enhanced Sharing : Upcoming features suggest a focus on sharing and saving semantic layer insights to dashboards, streamlining collaborative efforts.
Search Functionality : Improved search capabilities for metrics and dimensions are anticipated, simplifying the discovery process for users.

The evolving roles of semantic and presentation layers are central to the development of more intuitive and powerful data analytics tools. As these trends continue, we can expect a more seamless and collaborative data experience for all users.

Explore the latest rankings of BI tools with a focus on Lightdash's performance and features.

What is a Semantic Layer? (and why you need one)

What is a Semantic Layer

A semantic layer exists to present data to users as a set of related and commonly understood business entities, terms and metrics.

A semantic layer is typically the “top” layer of a data warehouse /lakehouse. It is accessible to end users and report developers, who use it as the source for reports, dashboards, and ad hoc analysis.

A semantic layer is important because it fosters a shared understanding of information across the business. It supports the autonomous development of consistent reports, analysis and dashboards by end users.

A semantic layer is:

First, a business glossary . The basis of a semantic layer is a glossary of related commonly understood business entities, terms and metrics, e.g., The concepts of a Customer, Product, Sale and the relationship between Sales and Products, and Sales and Customers.
Represented as a “business view” of data . Physically, the “business glossary” exists as a “business view” of enterprise data. The “business view” is implemented in the semantic layer. The semantic layer is typically the highest/final layer of a data warehouse or data lakehouse implementation. The raw enterprise data undergoes a series of transformations to arrive at the semantic layer’s “business view” format. The most common “business view” format is a dimensional model .
Accessible to BI developers and end users . The semantic layer is accessible to end users, data analysts and BI developers. It’s the source of data for reports, ad-hoc analysis and dashboards.
Supports Autonomy . A semantic layer supports autonomous access and navigation of the data. It supports self-service BI and ad-hoc (drag-drop) analysis.
Fosters a shared understanding . A semantic layer fosters a common understanding of enterprise data amongst all business users.

Why do you need a Semantic Layer?

In large and small businesses today, there are challenges in producing consistent reports.

Without a semantic layer, users, data analysts and BI developers inevitably use data from a variety of sources to develop reports, etc. These reports will often contain conflicting definitions and calculations. These contradictory reports generate a lot of confusion. Business users waste time arguing about these differences with little possibility of resolution. Ultimately, without a semantic layer, the quality and trust in the data on which decisions are made is eroded.

In contrast, semantic layers provide a shared understanding of business terminology and a “single source of truth” for reporting etc. Users, data analysts and BI developers use this “single source of truth” as the source of all reports, etc. This promotes a common business language and understanding and fosters trust, quality, and user collaboration. In addition, it encourages reuse and reduces duplication of effort and waste.

Physically, semantic software in a Data Warehouse or Data Lakehouse architecture provides the following benefits:

High-performance aggregated queries . Sub-second response time to aggregated queries over billions of rows.
“Cleaning up the model” to Hide tables, columns, and relationships irrelevant to the business.
Adding Hierarchies. Hierarchies enable hierarchical reports, drill-downs and more straightforward navigation.
Adding reusable context-aware calculated metrics.
Renaming tables and columns if necessary (although your data warehouse, if modelled correctly, should already use the correct naming standard).
Unified Data . Semantic models can combine data from multiple sources. Indeed, an end user could enhance the data warehouse with their own data source in the semantic layer.
Support for BI tools . Many BI tools natively support connecting to and querying semantic layer software.
Context-Aware Security . Restrict data access based on tables, rows, columns and formulas.

The different Types of Semantic Layers

The semantic layer tends to fall into four categories:

1. Data Store or fat semantic layer

The Datastore flavour contains a complete additional copy of the Data. The data is stored in a proprietary, highly compressed and optimised format (usually column store). This flavour comes with an internally optimised vector-based query engine. The Datastore flavour tends to perform better than the Virtual flavour because these data stores are structured to support high-performance aggregated queries. The emphasis is on aggregated. The data store is usually transient and re-loaded periodically from the Data Warehouse. Example technologies include Microsoft Azure Analysis Services, PowerBI data sets, Kyligence, GoodData, Apache Druid and Apache Pinot.

High performance for aggregated queries
Easier to define security.
It contains functional query languages that make it possible to define complex metrics.
Increased complexity and cost to load an additional datastore.

2. Virtual or thin semantic layer

The virtual semantic layer doesn’t store a separate copy of the data (although it might cache data for performance). Instead, the virtual semantic layer contains the logic that defines the semantic model. When a query is executed, the virtual semantic layer acts as a proxy and generates and runs an SQL query against the underlying data source(s) . Examples of technologies include Cube, Malloy, LookML, Metriql, AtScale, MetricFlow, Metlo, and Denodo.

Avoids data movement to an additional data store.Lower complexity.Lower cost due to less data storage.
Tend to be slower than data store-based semantic layers optimised for aggregated queries. Caching and pre-computation can elevate some of this disparity, but not all; frankly, it moves this solution toward the hybrid option anyway.

3. Hybrid semantic layer

Hybrid flavours support both datastore and virtual modes. Developers can generally define which tables within a semantic model are stored vs virtual. This can help with the trade-offs of performance vs complexity/capacity. In some cases, the data may be so large it is beyond the capacity of the semantic platform to store it. It could be argued that virtual caching is one way to achieve a hybrid model. A better example is Power BI datasets that allow a single semantic model to have a mix of stored vs virtual (direct query) tables.

4. Meta Semantic layer

This is a relatively new development in semantic modelling. Effectively, companies like dbt allow developers to define metrics in a platform-agnostic language. The idea is that dbt can generate the code for a given metric for any supported platform. That’s the idea, at least. Think potentially generating a Power BI dataset or another language for a different platform. This provides portability. It’s very early days, and so far, dbt’s implementation is more focused on supporting its own dbt cloud semantic server. However, the original open-source and open-platform idea persists. Time will tell. dbt Semantic Layer | dbt Developer Hub (getdbt.com)

How are semantic layers implemented?

From a physical point of view, a semantic layer is implemented using specialized software. There are several flavours, which we will expand on in this section.

1. Semantic layer implemented within a BI tool

Modern BI reporting and analysis tools like Power BI, Tableau and Qlik allow data analysts to model a semantic layer directly within a dashboard or report. Some tools will enable you to deploy the semantic model independently from a report and have it act as the semantic layer that many dashboards and reports share.

Ultimately, this approach has issues. An undisciplined proliferation of separate semantic models within dashboards and reports defeats the purpose of a shared semantic layer. What is needed is a disciplined development approach that enforces shared data models.

Power BI has the most comprehensive use case as a semantic layer among the most popular BI tools. While it can support a semantic layer coupled with a report, it can also support a scenario as a stand-alone semantic layer. First, a data set can be published independently from a report and shared by many reports. Power BI premium capacity supports large data sets beyond the typical 10GB up to the capacity size. Power BI has an XMLA endpoint and a REST API that allows external applications to make queries. It enables users to create composite models to supplement data, e.g., a budget Excel spreadsheet combined with actuals from a data warehouse. Power BI supports stored, virtual and hybrid query models. People have even used Power BI as the semantic layer and Tableau as the client via XLMA endpoints. It also has a highly capable functional language, DAX, an internal Vertipaq/tabular data store, and a query engine.

In contrast, Tableau’s strength is its visual aesthetics. It does support some semantic modelling concepts in its data model. It also supports high-performance queries with its Hyper query engine. However, it lacks the concept of publishing a data model as a stand-alone semantic layer. It also lacks the rich analytics language like DAX.

Semantic Layer implemented in a Data Warehouse/Data Lakehouse Architecture

The semantic layer is the final layer of a data warehouse/data lakehouse architecture.

The semantic layer sits between the presentation layer (the gold layer in Data Lakehouse) and the reports/analysis/dashboards. End users can view, navigate, and query the semantic model through a BI tool.

The best practice is to model the presentation/gold layer as a dimensional model. A dimensional model star schema lends itself nicely to semantic layer entities. The core of a dimensional model is business processes, which are modelled as facts with their measures/metrics. The business entities involved in business processes are modelled as dimensions with their descriptive attributes.

Most semantic modelling work and transformation occurs in the presentation/gold layers. The semantic layer should map directly to the Facts and Dimensions in that layer.

So, if the semantic layer looks like the presentation layer, why need a separate semantic layer?

As discussed before, the semantic layer software offers:

High-performance aggregated queries .
Hiding tables, columns, and relationships irrelevant to the business, including surrogate keys and management columns.
Adding Hierarchies. Hierarchies enable hierarchical reports, drill-downs and more straightforward navigation. Adding reusable context-aware calculated metrics.
Unified Data . Semantic models can combine data from multiple sources. Indeed, an end user could enhance the data warehouse with their own data source at the semantic layer.
Supports BI tools . Many BI tools natively support connecting to and querying semantic layer software.
Supports ad hoc analysis . Provides an interface that BI tools use to enable ad-hoc, drag-and-drop, pivotable style data analysis. The data store and query engine behind semantic layer software makes this possible.

Universal Semantic Layer

A universal semantic layer is more about a philosophy or methodology. Personally, I advocate for always building a semantic layer on top of a data warehouse/data lakehouse, and, therefore, don’t endorse the “Universal Semantic Layer” concept.

“Universal Semantic Layer” advocates believe building a data warehouse requires too much effort. They advocate skipping the data warehouse build. Instead, you connect your semantic layer directly to source systems or a raw dataset in a common data store. Then, the semantic layer is used to join and transform that data into the semantic model. When a user accesses the semantic layer, the semantic layer software generates and orchestrates the queries across many sources and joins the disparate result sets to produce the final result.

This approach often lends itself to the “virtual” type of semantic layer software, where the semantic layer is just a thin veneer with a semantic model.

An obvious issue with this approach is performance. The underlying data sources are not specialised in aggregated queries. Joining across source systems often leads to inefficient query plans. The queries can take minutes or hours to return results. To counter this issue, vendors introduce pre-processing and caching, but frankly, this can only achieve so much. Some have even taken to embedding an analytics database behind the virtual semantic model, shifting source data to this analytics database. This sounds a lot like a data warehouse… just worse.

Rather than saving the developer’s effort, it simply shifts it from the data warehouse/data lakehouse (where it belongs) to the semantic layer. The semantic layer is not well suited to implementing transformations. It also shifts the processing workload from batch/overnight data warehouse loads, where the data is transformed and prepared by the data warehouse/data lakehouse for the semantic layer, to query time, when the end user has to wait for the transformation to occur while they run their report.

The semantic layer becomes the sole purveyor of the semantic model. The modern data warehouse is designed to support various usage scenarios besides ad-hoc analysis, reports and BI. Today, the modern data warehouse must support Data Science workloads, non-real-time data integration (i.e. reverse ETL), near-real-time analytics, etc. These workloads often require fine-grained and/or high-volume data rather than aggregated data. Semantic layer software excels at aggregated queries but not fine-grained high-volume row-based ones. If you have a Data Warehouse/Data Lakehouse, use it to serve these fine-grained, high-volume, row-based queries. Then, use your semantic layer for what it is best for – serving aggregated analytical queries. Horses for courses!

The History and Future of Semantic Layers

Prepare for a rant!

Unfortunately, the concept of a semantic layer got lost in the vendor-driven, VC-backed “data warehouse is dead”, data analyst-centric collective nightmare of the modern data stack that valued agility over all else! In the melee, we lost the consistency of meaning, reuse and sustainable data assets that data warehouses were designed to deliver.

The problem with data warehouses was never the concept; it was the execution . Back in the ’90s and ’00s, business users were very frustrated by how long it took to create what seemed like a fairly simple report. Couple that with the explosion of data that stressed the state-of-the-art databases at the time, and it’s true that something had to change.

Personally, I believe we should have (and could have – with automation) addressed the methodology of delivering data warehouses. Unfortunately, the data warehouse baby got thrown out with the bath water. Instead, we got “Big Data” (Remember Hadoop, etc.) and Self-Service BI. The “Big Data” hype cycle was driven by the very largest organisations (the Facebooks and Googles of the world) and pumped by the consultancy industry onto organisations that didn’t have the same big data issues. Consultancies love this kind of disruption. It puts bums on seats and $ in the bank. “Big Data” was a monumental flop, but out of the ashes emerged HDFS and Spark. Spark is the open-source distributed computing technology underpinning Databricks, Microsoft Fabric and other analytics platforms. Self-service BI was a myth that gave organisations the excuse to favour agility over sustainability. The result was “data swamps” and a reemergence of the “single version of the truth” problem and other issues that data warehouses were designed to fix.

However, now, finally, things are changing. We are seeing the signs of the industry awakening from its coma . The emergence of the Data Lakehouse architecture acknowledges that the Data Warehouse concept is still valid. The Modern data warehouse, I think, is still overly complicated, but now, rather than being purely reporting/dashboard/analysis focussed, we have a unified platform that can serve more usage scenarios like Data science/analysis, real-time analytics and ETL, operational system analytics APIs, embedded BI and reverse ETL.

Even better news: the industry is recovering from its amnesia around semantic layers . In the 90s and 00s, semantic layers were regularly implemented with software like Microsoft Analysis Services, Business Objects Universe, etc. Unfortunately, semantic layers didn’t fit the narrative and were discounted and “forgotten”. Thankfully, the semantic layer is making a renaissance, as it should, as businesses encounter the reemergence of the problems that data warehouses were designed to solve.

There is new (and existing) software that is focused on implementing a Semantic layer. The emerging software sometimes uses new terms (e.g. Headless BI, Metrics Layer) as synonyms for a symantic layer. Headless BI may be slightly different as it implies that the semantic layer is not delivered within or alongside a visualisation platform.

The software can be divided into roughly 3 groups:

Stand-alone tools like Cube, AtScale, Denodo, Kyligence.
Embedded within a Visualization platform. E.g. GoodData , PowerBI, LookML .
Meta or Virtual layers like the dbt offering. In dbt Core/Cloud, it’s possible to define metrics over your models using a metrics language. Dbt is developing a dbt Server in dbt Cloud that allows a visualisation client to connect in real-time, get metadata about available metrics, and then query the dbt server. The dbt server interprets the query using the metric definitions, generates an SQL query, executes against the underlying database, and returns the result. Other similar tools work in a similar way but have other strengths. Examples are Malloy , Metriql , MetricFlow , and Lightdash .

Semantic later software has needed to evolve from the narrow BI focus of the 90s and 00’s to serve a more diverse set of usage scenarios of the modern data warehouse. A semantic layer must now be accessible for more usage scenarios like Data science/analytics, operational embedded BI, reverse ETL, etc.

Thankfully, nearly all of the above tools have responded to this need. Many provide APIs, SDKs for Python, JDBC/ODBC interfaces, Robust metrics language, etc. Semantic layers are now able to play a wider role in promoting a common business language for end users and data professionals across any usage scenario.

The future for semantic layers is looking bright!

Adam Gilmore is a Data Architect with 20 years' experience in the Data field. He writes for dimodelo.com and develops courses covering Data Analysis to Data Architecture.

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What is a Semantic Layer? (and why your BI stack needs one)

Defining the four parts of a complete, universal semantic layer.

We’ve all seen the massive explosion of technologies and their applications in the world of data.

Over the past 15 years, the modern data stack has experienced a remarkable evolution. It started with traditional relational databases and on-premises data warehouses. However, with the rise of big data, cloud computing, and advanced analytics, the data stack rapidly transformed.

New technologies like Hadoop, Spark, and NoSQL databases emerged, providing scalable and efficient solutions. Additionally, the advent of data lakes, machine learning, and real-time processing further revolutionized the data stack.

And today, we witness the dominance of cloud-based data platforms, automated data pipelines, and powerful data visualization tools, enabling organizations to unlock valuable insights and drive data-centric decision-making.

So the question is—in the age of more data tech, how does an engineer unite and integrate so many different sources and tools to form a cohesive pipeline? One that’s versatile and use case agnostic, that can enable applications ranging from BI to customer-facing embedded analytics, but abide by software engineer best practices, efficiency, consistency, and—with the ever-changing landscape—future-proofing?

The answer lies in the semantic layer. We’ll explain.

What is a Semantic Layer?

A semantic layer is a middleware that sits between your data source and downstream applications. It abstracts raw data and presents it in a meaningful way to the end-user and serves as a contextual filter between the data warehouse and the various analytical tools that businesses use to slice, dice, and analyze their data.

The key word in there is ‘context’: a semantic layer contains pre-defined business rules, data definitions, metadata, and other relevant information, making it easier and faster to query data effectively. It standardizes the vocabulary of the data, making it consistent across all reporting tools and data sources. It also simplifies the process of data curation while preserving data integrity and performance.

But wait—there’s more.

At Cube, we often refer to the “complete, universal semantic layer.” Because we believe a semantic layer should be just that and isn’t a whole semantic layer if it isn’t exactly that.

The ‘completeness’ aspect applies to a semantic layer’s feature set and capabilities to support all of the critical metadata that must be consistent across a data stack, including metrics definitions and data modeling, as well as data access control and performance management.

And the term ‘universality’ in there implies that a semantic layer must solve the ‘many-to-many’ problem we see now in the data landscape. Meaning, it must be universally compatible and able to bridge the massive variety of data sources and downstream applications to allow engineers and developers to curate the best possible stack for their use cases.

The Parts of a Complete, Universal Semantic Layer

A complete, universal semantic layer should have the following four layers:

Data Modeling : a layer in which metrics definitions and data models are set. Where you can organize data with meaningful context to ensure that the different downstream applications consuming it produce consistent insights.

Data Access Control : a layer in which consistent security context is orchestrated upstream of every downstream application. Enforcing data access controls and governance up stack ensures every end-user is accessing only the appropriate data.

Caching : a layer that stores data, serving as a buffer for the data source to bolster high concurrency, avoid latency, and maintain application performance.

APIs : a layer that ensures and streamlines compatibility between varying data sources and downstream applications.

Why your modern data stack needs a Semantic Layer

Data consistency.

A semantic layer plays a crucial role in achieving data consistency throughout a stack. By encompassing predefined metric definitions and comprehensive data models, it ensures a cohesive and standardized approach to handling data across various layers and components of a system. This helps in promoting seamless integration, efficient analysis, and robust decision-making processes.

Data Security

By granularly defining and controlling access to data at the semantic layer level—in a centralized place, upstream of every application and data consumer—companies can ensure that data is only accessible to authorized users and that sensitive data is protected. This reduces the risk of data breaches and internal mishandling and reinforces compliance with data privacy regulations.

Data Performance

When a semantic layer includes a caching layer, it serves as a buffer to the data source. Instead of serving many concurrent and often redundant queries, the caching layer within a semantic layer can serve as a buffer to the data source. In doing so, it can optimize performance and response times—which is particularly important for embedded analytics and AI-based applications, where real-time data processing and analysis can significantly impact customer business outcomes and user experience.

Stack Flexibility

Given its API layer and abstraction of data logic from the presentation layer, a semantic layer enables companies to pick and choose the tools that best suit their needs—such as database technologies, visualization tools, and data analysis software—without sacrificing innovation and time-to-market.

Time-to-value

By empowering engineers to skip manually orchestrating data modeling, caching, and data access control per application, a semantic layer dramatically reduces time-to-value and enables extremely fast-to-market application deployments. It also standardizes the data analysis process and eliminates the need for manual data restructuring, both speeding up the data modeling process. Plus, it significantly improves the developer experience—and just makes your engineer’s day better.

Future Proofing

With a semantic layer, you can easily adapt to changing business requirements and add new data sources without disrupting existing processes. It provides a flexible and scalable foundation, ensuring your stack can evolve alongside technological advancements. By embracing a semantic layer, your data stack becomes future-proof, empowering your organization with agility and intelligence to thrive in the data-driven era.

What are the use cases of a Semantic Layer?

The use cases of a semantic layer are plenty. Really. But today, we’ll focus on these three:

Embedded Analytics

With a universal semantic layer, cross-stack incompatibility between varying sources and tools is fixed, allowing engineers to curate the stack that’s best for their use case.

Moreover, it solves the old dilemma of “Do I embed inflexible, non-native, generic iframes into customer-facing applications because it’s accessible, or do I spend everything I have on building from scratch and maintaining a custom interface?”

With a semantic layer as a basis for embedded analytics , companies can deliver custom data experiences to users that are affordable to develop and maintain. Plus, having users query from the semantic layer—rather than directly from the source—dramatically boosts application performance and reduces data warehousing costs. (Here’s a case study to illustrate how.)

Semantic Layer for BI

According to Forrester , the average company in the US uses four or more BI tools (with 25% using ten or more) . And manually orchestrating data modeling, caching, and data access controls for each opens the door wide open for gaps, insight misalignment, and security breaches.

By managing all of these things upstream of every internal analytics application, on the semantic layer level, companies can not only save their data engineers a massive headache—they also trust that insights are consistent and accurate, no matter from which tool or team they originate.

AI & LLM-based applications

With the seemingly sudden roar of AI-powered data experiences, companies are rushing to build their own. This presents two problems: a) building LLM is complex and expensive, and b) building accurate LLM is difficult. But there is a solution to hallucinating AI , and you guessed it—it’s the semantic layer.

With the comprehensive data context provided by a semantic layer, an LLM doesn’t have to navigate complex joins and metrics because they’re already abstracted and translated into a simple interface based on business-level terminology. The easy integration of a company’s proprietary data with the common knowledge used by an LLM also fixes the garbage-in, garbage-out problem.

Moreover, the caching layer of a complete semantic layer boosts query response times. And lastly, the security context it provides blocks direct access between AI and raw data stores, allowing it to generate SQL through the semantic layer rather than executing it in the data warehouse.

To sum things up.

At this point in the evolution of the data landscape, a semantic layer is a necessary and critical component of the stack. The days of the ‘Many-to-Many’ problem and manual data modeling, security context, and caching per application are (thankfully) growing distant behind us.

To learn more about how a complete, universal semantic layer could radically transform your stack— say hello . We love to chat :)

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July 5, 2022 — 9 min read

What is a semantic layer, and how does it turn your data into knowledge.

It’s taken as read that the data created by your organization is incredibly valuable. Collecting and analyzing your data can lead to improved customer insight, increased market intelligence, forward-thinking innovation, improved business operations, and too many other business benefits to count .

But as ubiquitous as your data is, it’s worthless if your teams don’t understand it.

Most professionals aren’t technical experts in data engineering or data analysis. But they shouldn’t have to be in order to draw insights from your business’ data. In order to make knowledge gleaned from your raw data accessible to your organization at large, it’s crucial to provide a business-friendly view that allows anyone in your organization to quickly and easily find answers to their business questions.

Enter the Semantic Layer, aka “the Knowledge Layer”

In and of itself, data has little value. It’s the knowledge that can be gleaned from your data that’s worth its weight in gold. And that’s why it’s crucial to build a layer of knowledge — the semantic layer which consists of the key business concepts and the relationships between them about your organization. The semantic layer should live in your data catalog because that is where it’s defined, explained, and mapped to the source data.

According to Domo’s CDO, Mohammed Aaser and data.world’s Principal Scientist, Juan Sequeda, 97 percent of workers at most organizations are non-technical and unable to work with raw data . But these workers still need to be able to access key data to rapidly answer business questions. Within a data catalog , a semantic layer uses common business language to empower these non-technical workers to find the data they need, understand what data they’re looking at, and how it relates to all facets of your organization.

Saving Time and Resources As You Scale

The semantic layer enables consistent, org-wide understanding and definitions of your data , ensuring everyone’s using the same internal language to describe the same thing. Not only does this make your data easier to find and understand across your business, it also prevents time-sucking duplicate work; Businesses often start data projects from scratch, even when much of the analysis has previously been done, simply because different teams within your organization use different data definitions; For example, teams may use differing terms like “Active customer” or “customer,” without understanding their internal definitions actually refer to the same thing. And often, Team A might have already done the analysis Team B is hunting for… but because they’ve defined the data differently, they don’t realize the work has already been done. This constant reengineering and rework adds up, particularly in organizations attempting to scale up their data use and data-driven decision making. But according to Aaser, if teams simply arrived at common definitions and understanding of their data, organizations could avoid redundant work and reduce the time they spend on data projects by 70 to 80 percent. Just imagine the knowledge your data teams could unveil with all that time… Cue the semantic layer, which dramatically simplifies queries by creating a logical view of your data using business-friendly terms. The semantic layer skirts the complexity of your vast amounts of data, empowers everyone in your organization to find exactly what they’re looking for, and ensures you’re all speaking the same data language.

A semantic layer as represented by a knowledge graph

Building a Semantic Layer over Your Data

Building a semantic layer can be a complicated process. But at data.world, we’re firm believers in making it as easy as possible by representing the semantic layer visually. This visual representation is called a knowledge graph , and — among other benefits — it provides a birds-eye view of your data’s semantic layer and how it’s all connected.

Let's get technical: understanding the semantic layer

As mentioned above, the semantic layer acts as a bridge between underlying data sources — your data warehouse, data lake, etc. — and users who need to access and analyze that data: data engineers, data analysts, business users, etc. It serves as a virtual layer that abstracts the technical complexities of data sources and presents a simplified and unified view of the data in familiar business terms.

The components of a semantic layer include:

Data sources:

The semantic layer connects to various enterprise data sources — such as a database, data warehouse, data lake, and other data storage systems — where the raw data resides.

Data models:

Semantic layers include data models that define how data from different sources is structured, transformed, and combined to create a unified view of data. These data models provide a logical representation of the underlying data, making it easier for business users to access and understand.

Business logic and rules:

The semantic layer may include business logic and rules that are applied to the data to ensure consistency, accuracy, and adherence to business rules. This allows business users to access pre-processed data that is consistent and compliant with their organization's policies.

Semantic layers include metadata, which provides additional context and information about the data, such as data lineage, data quality, and data definitions. (Metadata helps in understanding the data and ensures data governance and security.)

The benefits of the semantic layer in data analysis

The semantic layer aids in data analysis for numerous reasons, including:

Simplifies data integration and abstraction:

The semantic layer consolidates data from multiple sources, abstracts the technical complexities of data sources, and provides a unified view of data. Data engineers can use the semantic layer to integrate data from disparate sources such as databases, data warehouses, and data lakes, eliminating data silos and creating a unified view of data. This simplifies the data integration process, allowing data engineers to efficiently combine data from diverse sources and make it accessible to data analysts and other business users.

Enhances data understanding and accessibility:

Semantic layers provide a common business vocabulary familiar to business users, bridging the gap between technical data and business users enabling them to access and understand data easily. They define business objects and their relationships, creating a semantic model that represents the underlying data in business terms. This business representation empowers the use of self-service analytics and BI tools tools to explore and analyze data in a way that aligns with familiar business terms, meaning business users can understand the data without needing technical expertise.

Semantic layers provide a common business vocabulary, empowering business users to understand technical data.

Facility data governance and security

Data governance and security are critical aspects of data management. The semantic layer ensures data consistency and accuracy by applying business rules and logic at the data layer. This helps in maintaining data integrity and enforcing data governance policies across the organization. Additionally, the semantic layer provides a secure access layer, allowing organizations to control and manage data access based on user roles and permissions. This ensures that only authorized users can access the data, enhancing data security and compliance.

Real-world Examples of Semantic Layers

Let's look at some real-world examples of how businesses leverage the semantic layer to unlock the power of their data.

Business Intelligence and Reporting

Organizations can leverage a semantic layer to create a rich semantic model that serves as a "single source of truth" for business data. Business intelligence (BI) tools can connect to this semantic model to create data visualizations, dashboards, and reports. Business users can easily access and analyze data using familiar business terms, enabling them to gain actionable insights and make informed decisions. Self-service reporting and analysis become possible, allowing users to create their own reports and share them with their teams.

Data-Driven Decision-Making

Predictive analytics is a powerful tool for organizations when making data-driven decisions. The semantic layer can be used to integrate historical data with external data sources, creating a rich data set for data scientists to build predictive models. The semantic layer provides a standardized and consistent representation of data, making it easier for data scientists to analyze and model the data, and the resulting insights can then be used by business users for strategic planning and decision-making.

Data Integration and Data Lakes

Data lakes are a popular choice for storing and processing large volumes of data, but they can be complex and challenging to navigate. A semantic layer can be used to ingest data into the data lake, creating a unified view of data and providing a common business vocabulary for data discovery and exploration. This simplifies data integration and data access for business users, enabling them to easily explore and analyze data in the data lake without needing to understand the underlying technical details.

The power of the semantic layer

The semantic layer is a powerful tool that bridges the gap between complex data and actionable insights.

The semantic layer is a powerful tool that empowers businesses to bridge the gap between complex data and actionable insights, and it's time for your businesses to embrace the power of the semantic layer and leverage it to gain a competitive advantage in the ever-evolving world of data analytics.

By leveraging a semantic layer in your data platform, your business can unlock the true potential of your data assets, enable self-service analytics, and drive data-driven decision-making throughout your organization. By implementing your own semantic layer or leveraging a universal semantic layer, every member of your business can gain actionable insights and equip your teams to make informed decisions based on a common understanding of the data. With the right tools, such as business intelligence (BI) tools, you can harness the power of the semantic layer to empower your data engineers, data analysts, data scientists, and business users to efficiently access and analyze data for their specific needs.

As the landscape of data workloads continues to evolve and become more complex, the semantic layer sits at the heart of data integration and data understanding, providing a rich semantic model that unlocks the true value of data storage and data access . It'a a critical enabler for business units across your organization to collaborate, analyze data, and gain insights from all your data sources.

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Jun 16, 2023 / Jeff Smith/ in Guest Blogs

The universal semantic layer: more than enough.

ABSTRACT: A "universal" semantic layer delivers uniformly defined business metrics across the data stack, but a BI solution may offer a cost-effective alternative.

According to a 2022 analysis of large manufacturing and CPG companies, 97 percent of employees are not “data practitioners.” Yet many of these workers are required to make decisions based on data they can (or want) to access themselves.

A semantic layer can help this majority of less-technical workers gain immediate and consistent value from data.

What is a semantic layer? What is its value?

A semantic layer is a logical representation of underlying data that defines commonly used business metrics in terminology that can be easily and consistently understood among all users, especially non-technical users. Conceptually, the semantic layer resides between data sources and the presentation layer, the latter of which may include one or more business intelligence tools, notebooks, data apps, or data science tools. However, semantic layer functionality may either be included as a feature of a cloud data warehouse or a BI platform, for example, or exist as a standalone, “universal” solution.

To illustrate the semantic layer concept by example, let’s consider a calculated business metric, monthly sales commissions per region . A semantic layer could let business users quickly access monthly sales commissions per region figures using a BI tool’s query interface, even though such figures are calculated according to a specific formula using data from multiple sources that identifies sale location, employee, transaction amount, currency, time of transaction, and so forth. Once the business defines the semantic layer, monthly sales commissions per region becomes a reliable and quickly accessible metric for use across the business.

A semantic layer abstracts the end user from the form, location, and complexity of data to deliver the following benefits.

Enable self-service and collaboration by business users.

Reduce dependency on IT and data support resources.

Minimize organization-wide inconsistency and duplication of effort in calculating business metrics.

Speed time-to-insight.

Depending on the semantic layer’s location and capabilities, it can also improve analytic query performance and reduce cloud-data egress fees by automatically precomputing and storing frequently used business metrics.

How is the semantic layer different from similar data engineering concepts?

As with many IT terms, the precise definition and usage of “semantic layer” are not monolithic. Some IT practitioners and writers use the terms s emantic layer and metrics layer ( or metrics store ) interchangeably. At minimum, a semantic layer is a metrics layer that contains reusable, commonly used data incorporating measures and dimensions (also known as categories).

In addition to metrics definitions, various IT vendors and influencers add capabilities to the concept of semantic layer, which may include the following.

Caching of frequently used metrics.

Metadata that supports data lineage, data quality, and data definitions.

Access control based on permissions granted to users and roles.

APIs (SQL, REST, etc.) for querying data and modifying semantic definitions.

Though practical in value, the semantic layer is an abstract concept. For clarity, we should distinguish it from the data catalog. Like a semantic layer, a data catalog centralizes business metrics. However, it also emphasizes the organization and retrieval of physical data assets rather than metrics and their logical data associations. Further, and unlike a semantic layer, a data catalog includes search capability for accessing various assets, such as data models and dashboards.

Though practical in value, the semantic layer is an abstract concept.

The semantic layer should be understood differently from data virtualization and data federation, as well. Like a semantic layer, virtualization and federation are abstractions from source data. Unlike a semantic layer, they seek to create a representation of all underlying data rather than convert particular data elements into understandable and uniformly determined business metrics.

Why is the semantic layer attracting greater interest today — especially a “universal” semantic layer?

The semantic layer is not a new concept. Prior to its acquisition by SAP in 2007, BI platform vendor Business Objects introduced the concept of the semantic layer. SAP later named this semantic layer functionality the BusinessObjects Universe.

OLAP analytical data engines, such as SAP Business Warehouse and Microsoft SQL Server Analysis Services , offered a semantic layer in their cube structures in which measures, dimensions, relations, and calculated measures were defined.

In the past decade, BI tools such as Tableau introduced their own integrated semantic layer; Google Looker’s LookML language especially popularized the use of the semantic layer. Such semantic layers have customarily been limited to their proprietary BI platforms, though we will return to this point.

In 2021, interest in the semantic layer concept intensified when Mode founder Benn Stancil observed that an environment-wide or “universal” semantic layer represents the missing element in the modern data stack.

Based on a diagram by Benn Stancil

The market entry of such tools as Cube , MetricFlow , MetriQL , and AtScale suggests a burgeoning interest in the benefits of a universal semantic layer. Such universal semantic layer offerings apply the DRY principle to all business metrics. This approach delivers the benefits of the semantic layer across the organization by consolidating into “one version of the truth” the business metrics that disparate presentation layer tools previously siloed (see diagram, above).

Spurring additional interest in the semantic layer in 2021, Airbnb published a detailed blog post to describe its internal semantic layer, Minerva . In 2022, dbt Labs launched its semantic layer and then acquired Transform Data in 2023 to enhance it.

Both the data storage layer and the data presentation layer of business IT environments have become increasingly complicated through a proliferation of data types and data consumption tools. According to a 2020 study , the average organization has almost four business intelligence solutions in use. Because of ever-growing IT complexity and the historical scarcity of semantic layers, market interest in the semantic layer — especially a universal semantic layer that centrally defines all business metrics and eliminates redundancies — has strengthened in recent years.

Finally, the semantic layer, especially the universal semantic layer, attracts greater interest today because it enables the modern data stack . The semantic layer facilitates self-service in a distributed data mesh architecture. A universal semantic layer, in particular, bolsters the implementation of a data fabric , as both the universal semantic layer and the data fabric seek to insulate end users from backend complexity while giving them access to all available data in understandable terminology and formats.

Should every organization implement a universal semantic layer?

As mentioned, a universal semantic layer offers the advantage of unambiguously and uniformly defined business metrics accessible across the data stack and the entire organization.

However, there are potential drawbacks. The time and expense requirements for implementing, integrating, and maintaining an added layer of the entire data stack are non-trivial. Organizations may require hiring and training a dedicated analytics engineer in order to sustain a continually updated universal semantic layer.

The time and expense requirements for implementing, integrating, and maintaining an added layer of the entire data stack are non-trivial.

These potential challenges must be weighed against the alternative disadvantages of maintaining isolated and duplicative semantic layers native in each of several tools.

Standardizing on a semantic layer built into an existing BI platform may be a sufficient approach that proves simpler and less expensive than a universal semantic layer, especially if a particular BI solution is used predominantly in the organization. Several major BI platforms have actually opened their semantic layers for use by other BI platforms to one extent or another.

MicroStrategy’s Federated Analytics lets users of Microsoft Power BI, Tableau, and Qlik leverage MicroStrategy’s semantic layer.

As analytics practitioner Aurimas Račas argues , Power BI is now capable as a semantic layer accessed by other data visualization tools that connect via XML for Analysis (XMLA) , including Tableau.

Finally, in March 2023 Google Looker announced Looker Modeler , a standalone semantic layer that defines and stores metrics for consumption in Power BI, Tableau, ThoughtSpot, and other platforms.

As universal semantic layer offerings gain awareness and customers, they prompt their older, BI-based rivals to take a more open approach to serving the full breadth of the data stack. This development unlocks additional options to organizations seeking cost-effective ways of realizing the benefits of a universal semantic layer.

Recommendations

A universal semantic layer promises significant advantages to business users and those supporting them. IT and data teams should consider the following factors in evaluating their potential.

The extent to which a production BI platform or other tool can function as a “sufficiently universal” semantic layer.

The risks, opportunity costs, and outlay costs of maintaining disparate semantic layers tethered to separate tools — and likewise the risks, opportunity costs, and outlay costs in software and staffing required to support a universal semantic layer. Considerations include the organization’s size, complexity of its underlying data, and scale of internal demand for consistently defined business metrics that can be accessed through a variety of presentation-layer tools.

Most vendors are eager to support proof-of-concept projects; these can realistically test assumptions and clarify the net benefits of adopting a universal semantic layer tool. As with most promising technologies, take seriously the potential value of a universal semantic layer, start small, and scale up in proportion to foreseeable benefits.

Author bio:

Jeff Smith is a freelance industry analyst and marketing consultant in IT and technical business-to-business offerings. Jeff's experience at Accenture, IBM, SAS Institute, Pyramid Analytics, and self-employment includes competitor and market research, product marketing, pricing, software engineering, and sales.

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Semantic and Presentation Layer of Big Data

In this Post, I introduced sematic layers and presentation or consumption layer with some programs as SQL Server Analysis Services (SSAS), Qlik Sense and Microsoft PowerBI business intelligence tools. Big data layers that are sources, data warehouse, semantic layer and, presentation layers display in this post. Some terms as Data Lake and warehouse component of big data are introduced [1].

Introduction

“Big data is a term that describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.” [2].

In this description, Big data is huge volume data in storages beyond of traditional data . This concept and data processing are commonly use in many different business area in today. Some of these are e-commerce, manufacturing, IOT, cloud systems.

Big data is stream from data sources to presentation layers. Data sources is the first step creation of big data which are huge volume data. When data reach to huge volume, some traditional solution is not run. Thus, some tools developed for big data solution (SSAS, Hadoop, Azure). In this data, there is some different data sources. Some of these are, IOT sources, e- commerce, network log, etc. Variant and heterogenous of data will be complex, huge volume and low velocity. Second step is Data warehouse. Data warehouses use same data sources. Data warehouse is replicated. This step created for set-up the semantic layer and processing of presentation layers. [3]

https://prologika.com/prologika-newsletter-fall-2013/

Semantic Layers

Semantic layer is the place where created relationship multi tables and preparing model for processing in presentation layers. This layer display the arranged data that acquired from data warehouse because of ETL (Extract, Transform, and Load). [4]. SQL Server Analysis Services (SSAS) tabular and multidimensional (OLAP) programs encounter of requirement for semantic layers. Data warehouses are replicated. It is most major in order to prevent missing data and, process in semantics layers [5]. This step is necessary in order to in process business intelligence programs. If there is direct connection storage and presentation layers, BI tools will be complex and low speed run because of in memory using. Model management will be harder due to direct to source using.

https://www.sqlshack.com/implementing-an-ssas-tabular-model-for-data-analytics/

Presentation and Consumption Layers

Consumption or presentation layer generate insight and roadmap for business area from result data. It makes sense of arrange data. Microsoft PowerBI, Qlik Sense and Tableau is commonly use in business intelligence tools. In this Big data Layer, BI tools make sure process within Presentation Layer or Consumption Layer. The acquired data are arranged and prepared with model in semantic layer or analytics and reporting layer. [6].

The outcome of the analysis is consumed by various users within the organization and by entities external to the organization, such as customers, vendors, partners, and suppliers. [7].

In this post, I tried to introduce semantic layers and presentation layer and importance of them. Complexity of source in big data concept, heterogenous data and variety types, replicated of data warehouse and importance, requirement of created semantic model for visualization and data business insight are written in post.

[1] Chessa, A., Fenu, G., Motta, E., Reforgiato Recupero, D., Osborne, F., Salatino, A., & Secchi, L. Enriching Data Lakes with Knowledge Graphs.

[2] TechAmerica Foundation’s Federal Big Data Commission, 2012

[3] Manikandan, G., & Abirami, S. (2017). Big data layers and analytics: a survey. In Computer Communication, Networking and Internet Security (pp. 383–393). Springer, Singapore.

[4] https://docs.microsoft.com/en-us/azure/architecture/guide/architecture-styles/big-data

[5] Satapathy, S. C., Bhateja, V., Raju, K. S., & Janakiramaiah, B. (2016). Computer communication, networking and internet security. Proceedings of IC3T, 5, 20160.(Pg-389)

[6] https://docs.microsoft.com/en-us/analysis-services/tabular-models/tabular-models-ssas?view=sql-analysis-services-2022

[7] Understanding the architectural layers of a big data solution — IBM Developer

Mustafa Kaynak

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Semantic Layer

A semantic layer is an interface sitting between data consumers and enterprise data sources, abstracting the underlying data architecture..

Users or their analytics tools input SQL statements and the semantic layer executes the query across data sources wherever they may be — no data movement required.

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By giving end users a single source of truth, the semantic layer breaks down data silos to deliver a holistic view of data across the enterprise to empower data-driven decisions. Let’s explore why semantic layers are so useful, how the semantic model differs from other approaches, and what role semantic layers play in modern data lakehouse architectures.

Semantic layer use cases for the modern data stack

Most enterprise data stacks are an amalgam of data platforms added by different business units at various stages of the company’s evolution. The semantic layer is key to federating this web of databases, data warehouses, data marts, data lakes, and other sources to support critical business use cases.

Self-service analytics

The complexity of enterprise data architectures poses a significant hurdle to driving data-driven business cultures. Few users understand the structure and nuances well enough to query sources effectively. As a result, small data teams with constrained resources become de facto gatekeepers, limiting access to only the highest-priority requests.

The semantic layer democratizes data access by abstracting that complexity away. It enables a self-service model that lets non-technical users, business intelligence analysts, and data scientists query sources directly without waiting for engineers to develop data pipelines.

Users throughout the organization can perform ad-hoc, interactive queries to generate insights and better business decisions. At the other end of the spectrum, data scientists can independently explore the company’s disparate data sources, shortening the cycle time of machine learning and artificial intelligence projects.

Single point of access for multi-source data integration

Another challenge with enterprise data is the complexity of consolidating data from disparate sources like transactional OLAP systems, traditional databases, and the cloud. Each source formats and structures data differently. Consequently, data engineers spend considerable time modeling these sources, even with tools like dbt to help manage pipeline development.

The semantic layer streamlines data discovery and modeling by giving engineers a unified view of all data sources. Standard SQL makes these sources accessible through tools like dbt, making it easier to automate pipeline development and testing.

Data governance

The heterogeneous nature of modern enterprise architecture also poses significant obstacles to data governance. Consistency and execution matter, especially when handling regulated data. Effective enforcement of access controls and data privacy rules is much harder when regions and business units don’t agree on basic business concepts like metric definitions or business terms.

Abstracting every data source through the semantic layer provides the consistent data definitions and metrics that governance systems need to enforce business rules and ensure compliance automatically.

Business intelligence (BI) reporting

One source of business friction arises when teams interpret and transform data differently. For example, miscommunication arises when the sales and finance teams calculate revenue differently. A semantic layer puts everyone on the same page, creating consistency in business terminology, assumptions, source data, and more.

Architectural opacity is another source of friction as it limits business intelligence (BI) analysts’ ability to discover and use data on their own. Forcing them to rely on data teams slows the decision makers that analysts support. A semantic layer lets analysts explore data across the enterprise. Moreover, they can use their existing BI tools, from Microsoft Excel to Tableau or Power BI, to create the dashboards and ad hoc reports their decision makers require.

Customer data platforms (CDPs)

A disjointed data architecture interferes with corporate strategies like Customer 360 or omnichannel marketing. Assembling a coherent view of customers wherever they interact with the business requires stitching together data sources across organizational and geographic boundaries. Each source has unique data structures, schemas, and quality standards, which require complex data pipelines to integrate into a customer data platform.

The semantic layer abstracts that complexity away, giving every customer-facing team access to a consistent and coherent view of customer activity.

Hybrid and multi-cloud data management

Despite all the benefits cloud computing offers, the move away from the premises introduces more data management complexity. Teams must manage data stored in different cloud services and applications in addition to traditional on-premises systems. Besides giving developers, admins, and engineers the access they need, companies must control access to protect their most sensitive data.

A semantic layer’s accessibility and governance capabilities simplify data management in hybrid and multi-cloud environments. Individual team members get access to the resources on demand. At the same time, role-based access control systems leverage the semantic layer’s rich metadata to reduce an organization’s attack surface significantly.

Data model vs semantic layer

Determining how a semantic layer will work requires a rigorous data modeling process that maps the underlying data source to how the business wants to use the data. Raw data in a data lake’s unstructured data stores will require rules to implement schema-on-read. Data stored in operational systems must be presented in consistent formats. Data’s provenance and lineage must be accessible. And rich metadata must be generated to maximize data’s utility.

Building a semantic layer on the foundations of well-planned data models ensures the elimination of data silos and prevents the architecture from becoming another data swamp.

Data products vs semantic layer

Data products and semantic layers are conceptually similar approaches to making data more accessible. Whereas the semantic layer exposes all enterprise data to support any use case, data products curate datasets to meet specific business needs.

Like the relationship between data marts and data warehouses, data products deliver focused, enriched views of specific datasets so users can get the information they need without the broader data landscape’s complexity. Organizations can use both approaches by building the business-focused data product on the semantic layer.

Semantic layer in data warehouses

When first developed, enterprise data warehouses promised to centralize business data to promote more effective analysis. Pipelines restructured data from operational databases into schemas that were easier to analyze. However, no single schema could address all the data the organization needed to analyze, resulting in multiple domain-specific warehouses. In most cases, these warehouses remain fit for purpose. Pain points emerge when integrating data from different warehouses for more sophisticated analysis.

A semantic layer lets organizations keep data in their legacy warehouse systems while abstracting each warehouse’s schema into a consistent set of metadata.

Semantic layer in data lakes

As the limitations of data warehouses became more apparent, the data lake emerged as a solution. These repositories could handle the unstructured raw data that was increasingly important to big data analysis. However, using a data lake requires significant expertise to understand and transform the data. To improve access, companies often use their data lakes as sources for an additional layer of data warehouses.

The semantic layer’s interface and metadata make the contents of a data lake more accessible, providing enough context to help users understand the data they’re looking at without having to call in a data engineer.

Universal semantic layer in data lakehouses: How Starburst helps

Starburst federates enterprise data sources to create a universal semantic layer, placing the entire data architecture at any user’s fingertips so they can query across data sources with ANSI-standard SQL and bring actionable insights into business decision-making.

Moreover, Starburst lets companies preserve their significant data investments. Rather than trying to create yet another single source of truth, a business implements Starburst to create a single point of access through a universally accessible semantic layer.

Stargate and Gravity are elements of the Starburst platform underpinning this semantic layer. Starburst Stargate enables global cross-cloud analytics, allowing companies to comply with data protection and sovereignty regulations without sacrificing analytics. Starburst’s universal discovery, governance, and sharing layer, Gravity streamlines data management with features like automatic cataloging, universal search, and role-based access controls.

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Using Semantic Layers with Data Marts: 5 Key Benefits

Semantic Layers Data Management

Key takeaways

A semantic layer simplifies data analytics by translating technical data into business terms.
Integrating semantic layers with data marts ensures consistent, robust and reliable data reporting.
This approach enhances business intelligence and streamlines data management processes.

A semantic layer acts as a translator between the raw, complex data stored in databases and the end-users who need to make business decisions.

This layer abstracts the underlying technicalities, presenting your data in terms that are familiar and meaningful to your business context.

Integrating a semantic layer with your data marts encapsulates the business logic, hierarchies, and calculations in a model, which standardizes data and ensures consistent reporting.

This integration not only enhances your business intelligence capabilities but also streamlines data management. You are then empowered to focus on strategic analysis rather than getting bogged down by the technical complexities of the data itself.

Semantic Layers Basics

Before diving into the details, you should understand that semantic layers are key to simplifying complex data environments for business users. They serve as an abstraction layer that translates technical data into business terms.

Defining Semantic Layer

A semantic layer is essentially a user-friendly interface that sits between your raw data and the tools you use to analyze that data.

It translates complex data from various sources into consistent, business-focused terms and views. Imagine it like a translator who knows all the technical lingo but speaks to you in clear, simple language.

A diagram illustrating the data processing process with semantic layers.

Image source: dbt Semantic Layers

Importance in Data Management

In the realm of data management , semantic layers play a critical role by providing a universal translation of metadata .

This means no matter where your data lives, you get a consistent understanding across the board. It helps to eliminate data silos , ensuring that all business users have the same perspective when making data-driven decisions.

Semantic Models: Components and Functions

Within a semantic layer, semantic models define the relationships, calculations, and hierarchies that give data meaning. Let’s break it down:

Components : These include tables, fields, measures, and dimensions.
Functions : They translate raw data into the business metrics you care about.

Business Logic and Abstraction Layers

Semantic layers enable you to embed business logic into your data workflows, meaning the data reflects real-world scenarios.

The abstraction layer shields users from the underlying complexities, presenting only the relevant information. This facilitates a smoother interaction for business users, who can then analyze data without diving into technical details.

Data Marts and Warehousing

In the dynamic ecosystem of data management, data marts and warehousing form the cornerstone of effective data strategy, offering you specialized storage solutions aligned with your organizational needs.

Overview of Data Marts

Data marts are focused data store subsections of a data warehouse tailored specifically to meet the needs of a particular business line or department.

Unlike the broader scope of data warehouses , data marts contain slices of historical data relevant to a specific area, providing you with a streamlined and efficient way to conduct data analysis.

Data Mart vs. Data Warehouse

You might consider a data mart as a subset that serves targeted business functions, while a data warehouse is a centralized repository for all of your organizational data.

A diagram showcasing the process of creating Semantic Layers and Data Marts within a data warehouse.

The distinction lies in scope and scalability: data warehouses integrate multiple data sources into a single source of truth , ensuring consistency and supporting large-scale analytics, whereas data marts provide quick, department-specific insights.

Single Source of Truth

Achieving a single source of truth (SSOT) through data warehousing means you have one definitive data store for all your users. It’s the central point from where all users get their data, crucial for maintaining the accuracy and integrity of historical data across an enterprise.

The application of data marts complements this by providing dedicated areas for focused analysis while still linking back to the central data warehouse.

Integrating Data Marts with Semantic Layers

Incorporating semantic layers into your data marts optimizes data accessibility and interpretation, ensuring that you leverage the full analytical potential of your organization’s data.

Modified Description: An illustration of Semantic Layers with Data Marts.

How a Semantic Layer Integrates with Data Marts

A semantic layer acts as a mediator between you and complex data marts. It transforms intricate technical data into a user-friendly format, allowing you to interact with data using common business terms.

This integration means you can query data without needing to understand the underlying database schema or SQL.

Overview of the Technical Process Involved in Integrating Semantic Layers with Existing Data Marts

The process begins with mapping data mart schemas to the semantic model. This mapping ensures that data remains consistent and accurate when you access it through the semantic layer.

Additionally, the management of metadata is crucial here, where descriptions, hierarchies, and calculations are defined, enabling the semantic layer to translate queries into the data mart’s language.

How Semantic Layers Can Be Built on Top of Various Types of Data Sources, Including Relational Databases, Spreadsheets, and Cloud-Based Platforms

Semantic layers are flexible and can be constructed atop different data sources. When dealing with relational databases , they work by using metadata to abstract the schema into business terms. For spreadsheets , they categorize and label data, facilitating effective data discovery.

For cloud-based platforms , semantic layers play a critical role in unifying disparate datasets from various locations into a coherent framework.

The Role of Extract-Transform-Load (ETL) Processes in Transforming Raw Data into Structured Formats Suitable for Semantic Layer Integration

ETL processes are vital for preparing your data for semantic layer integration. During extraction , data is collected from your various sources.

It’s then transformed where cleansing, deduplication, and conversion into the desired format occur.

Finally, the loading stage moves the processed data into the data mart. Once completed, your semantic layer can effectively interpret and serve the structured data.

Advantages of Using Semantic Layers with Data Marts

Harnessing the power of semantic layers within data marts translates into an array of benefits for your business.

Specifically, you’ll see improvements in data accessibility, enhanced reporting capabilities, and a significant boost in self-service analytics. Let’s explore how these layers underpin a robust, data-driven business environment.

1. Improving Data Accessibility for Analytics

Semantic layers simplify the way you access data for analytics, serving as a translator that turns complex data structures into business-friendly concepts.

For instance, instead of sifting through cryptic database fields, you’ll see streamlined categories like sales revenue or customer demographics . These layers work in tandem with data marts to provide targeted data subsets that are readily understandable.

User-Friendly Interface : With a semantic layer, users can utilize common business terms to navigate through data, avoiding the need to understand database schemas or query languages.
Consistent Data Understanding : It ensures that data is presented consistently across the organization, which means less confusion and more accurate analytics.
Faster Insights : By simplifying access to data, users can quickly gather insights without waiting for IT to generate reports, speeding up the decision-making process.

2. Enabling Advanced Reporting

Semantic layers empower you with the tools for advanced reporting . Extending beyond basic summaries, you can now leverage nuanced data views, which are particularly beneficial in specialized domains.

This alignment of business semantics with technical data structures makes reporting not only accurate but also more actionable .

Customized Reporting : Users can create customized reports that align with their specific business needs and goals, without relying on predefined templates.
Dynamic Data Exploration : Semantic layers allow for interactive reporting, where users can drill down into data, explore trends, and make discoveries on the fly.
Automated Business Logic : They apply business logic automatically, so reports include necessary calculations and aggregations, ensuring accuracy and relevance.

3. Self-Service Analytics Advantages

With the introduction of semantic layers, self-service analytics becomes accessible to a broader range of users within your organization. Users can query and explore data using familiar business terms, without needing to understand the underlying data schema.

A group of people standing around a computer screen discussing business intelligence with semantic layers

This removes traditional dependencies on IT for report generation, encouraging more frequent and relevant data interactions that support timely decision-making .

Empowerment of Non-Technical Users : Semantic layers provide an abstraction layer that translates complex data into business-friendly terms, enabling non-technical users to navigate and analyze data with ease.
Reduced IT Bottlenecks : With users able to generate their own reports and insights, IT teams are relieved of the constant requests for data, allowing them to focus on more strategic initiatives.
Rapid Decision Making : When business users can access and analyze data on demand, decisions can be made quickly and efficiently, keeping pace with the fast-moving business world.

Tips : If you are curios to learn more about semantic layers and data mangement, then check out all of our posts related to semantic layers and data management

4. Fostering a Data-Driven Culture

A data-driven culture is one where data is at the heart of strategic decision-making. Semantic layers encourage this culture by making data more accessible and meaningful to all members of an organization

As users across various departments gain the confidence to interact with data directly, their ability to contribute meaningfully to data conversations grows.

This inclusivity helps to democratize data and bolster a shared understanding of key metrics and objectives organization-wide.

Encouraging Curiosity and Exploration : With easier access to data, employees are more likely to explore, ask questions, and seek data-driven answers, driving innovation and growth.
Building Data Literacy : Regular interaction with data through a semantic layer helps improve the overall data literacy of the workforce, making data a common language within the organization.
Aligning with Business Goals : Semantic layers ensure that data is aligned with business objectives, enabling employees to focus on metrics that matter and contribute to the company’s success.

5. Enhanced performance

Finally, the coupling of semantic layers with data marts often leads to notably enhanced performance.

By predefining common calculations and aggregations, semantic layers ensure that the heavy lifting is done once, and the processed, business-ready data is served quickly for analytics purposes . This optimization translates to faster insights and more efficient use of your data infrastructure.

An isometric image of a data storage system designed for semantic layers and business intelligence

This setup streamlines the process of data retrieval and analysis, providing a more efficient pathway for delivering insights.

Optimized Queries : Semantic layers can optimize queries by abstracting the complexity of data marts. This means users can run reports and analytics faster, without compromising on the depth of data they can access.
Reduced Load on Operational Systems : By segregating analytical processes in data marts and simplifying access with semantic layers, there’s less strain on operational systems, leading to better overall system performance.
Caching Mechanisms : Semantic layers often employ caching mechanisms that store frequently accessed data, speeding up response times for common queries and reports.

Tools and Technologies

Within your modern data stack , semantic layers serve as the invaluable bridge between complex data sources, such as your data lake , and user-friendly business intelligence (BI) interfaces.

These layers abstract the technical details, so you can focus on analysis without worrying about underlying database queries.

Prominent Tools: From Tableau to Snowflake

A variety of tools exist to implement semantic layers effectively .

Tableau stands out for its ability to create interactive and shareable dashboards, while Snowflake excels in offering a cloud-based solution with a unique architecture designed for the cloud, streamlining data storage and analytics.

Both tools are integral to a comprehensive BI strategy , allowing you to manipulate and visualize data with greater ease.

A group of business people establishing and implementing semantic layers with data marts

Data Mart and Semantic Layer: The Essentials

It’s clear that this combination is a powerful duo for unlocking the full potential of your data. Semantic layers act as a translator between the technical complexities of data marts and the everyday language of business users.

By implementing a semantic layer on top of your data marts, you create an environment where data is not only more accessible but also more meaningful to those who rely on it to make critical business decisions.

Key Takeaways: Combining Data Marts with Semantic Layers

Simplify Data Access : Semantic layers provide a user-friendly interface, allowing business users to query data marts without needing deep technical expertise.
Ensure Consistency : A semantic layer ensures that everyone in the organization speaks the same data language, promoting consistency in reporting and analysis.
Enhance Data Governance : By centralizing data definitions and business logic, semantic layers contribute to stronger data governance and quality.
Improve Performance : Semantic layers can optimize query performance by abstracting the underlying complexity of data marts, making data retrieval more efficient.
Facilitate Self-Service BI : Empower your users with self-service BI capabilities, reducing the dependency on IT and enabling faster insights.
Support Scalability : As your organization grows, semantic layers help scale your BI efforts, making it easier to integrate new data sources and adapt to changing business needs.
Drive Data-Driven Culture : The ease of use provided by semantic layers encourages a data-driven culture, fostering more informed and agile decision-making across the company.

FAQ: Semantic Layers and Data Marts

What is the purpose of a semantic layer in a data warehouse.

The purpose of a semantic layer in a data warehouse is to provide a user-friendly interface that abstracts the technical complexities of raw data. It translates data into business terms, ensuring that end-users can interact with the data more intuitively.

Can you provide an example of a semantic layer in a data mart context?

An example of a semantic layer in a data mart might be a business-focused model that allows marketing professionals to query customer data using business terminology without needing to know the underlying database schema or query language.

How does a semantic layer differ from a data mart in data management?

A semantic layer acts as an interpretive layer that presents data in a business-friendly way, while a data mart is a subset of a data warehouse that focuses on a specific business line and often contains summarized data for quick analysis.

What are some common tools used to create semantic layers for data marts?

Common tools used to create semantic layers may include business intelligence (BI) platforms like Tableau and Power BI, as well as data modeling tools such as AtScale, which bridge the gap between raw data management systems and end-user accessibility.

Could you explain the architecture of a semantic layer within a data ecosystem?

The architecture of a semantic layer within a data ecosystem typically involves a middle tier that sits between the physical data storage (databases, data warehouses) and the presentation layer (dashboards, reports), translating complex data into understandable business terms.

What are the potential limitations or challenges when integrating a semantic layer with data marts?

Integrating a semantic layer with data marts can present challenges such as ensuring data accuracy and consistency, managing performance issues due to complex queries, and the potential need for specialized expertise to maintain and evolve the semantic model.

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April 21, 2022 | Posted by: Dave Mariani

The Semantics of the Semantic Layer

Blog / semantic layer.

I co-founded AtScale to focus on the challenges of supporting a large number of data analysts working on disparate sets of data managed in a massive lake. We borrowed the term “ semantic layer ” from the folks at Business Objects who originally coined it in the 1990s. The term was actually over 20 years old when we adopted it.

So what is a semantic layer exactly? If you Google the term, the following definition will pop up, which is a pretty darn good definition in my opinion (Google’s highlighted words, not mine):

Semantic Layer Definition from Wikipedia

The challenge is actually implementing a semantic layer in a way that just works.

We began building the AtScale semantic layer after working on big data from the trenches. We had to deal with the basic challenges of data scalability, query performance, metrics sprawl, complicated data pipelines and shadow business intelligence (BI). While the challenges seemed obvious to us, most of the industry was preoccupied with shifting data gravity to the cloud. With cloud data re-platforming in full swing, we are finally seeing attention turning to the last mile of enterprise analytics with the semantic layer topic surging in popularity.

The Semantics of a Semantic Layer

Cloud giants like Google and Snowflake , the unicorns like dbt Labs and a host of venture-backed startups are now talking about this critical new layer in the data and analytics stack. Some call it a “metrics layer” , or a “metrics hub” or “headless BI”, but most call it a “semantic layer” . I can’t tell you how happy it makes me that the industry is finally recognizing the importance of the semantic layer in a modern, cloud-first analytics stack. I couldn’t agree more that a logical, business-friendly view of data is what’s needed to make analytics accessible to everyone, not just data engineers and SQL jockeys.

While it might be just a matter of semantics, I prefer “ semantic layer ” over “metrics layer” , “metrics hub” , or “headless BI.” I think that the term “semantic layer” best describes this business-friendly data interface because it covers all types of data and use cases.

For example, the terms “metric store” and “metric layer” ignore the concept of “dimensions” altogether. Take a look at just about every BI tool on the market (i.e Tableau, Looker, Power BI) and they all include measures (or metrics) and dimensions in their interfaces. Metrics measure something but dimensions (i.e. “product”, “time” and “location”) categorize data by grouping or aggregating metrics. So, terms using “metric” are confusing and don’t map to how these layers will be consumed.

The term “headless BI” is also problematic because it only covers business intelligence use cases. A universal data layer is useful to more than just business analysts and BI. Data scientists need to access a consistent business-friendly interface to data for building and training their models. Furthermore, application developers who are building data-driven applications also need interfaces to data. As such, the term “headless BI” is inadequate because it only covers a single use case: business intelligence.

More Than Just Metrics

There’s a reason why independent semantic layers have taken time to come to market — building a semantic layer is hard. Yes, a semantic layer serves as a common metrics store or single source of truth, but there’s much more to it than that. For a semantic layer to be viable, it needs to:

Support any query tool, interface or protocol with a live connection to data
Express the most complex business logic (serve as a digital twin) using a semantic model
Deliver queries in under 2 seconds
Govern access to data for every query
Connect to any backend data store

The 7 Requirements of a Semantic Layer

The following diagram illustrates the core capabilities of a semantic layer:

Capabilities of a Semantic Layer - Diagram

1. Consumption Integration

For a semantic layer to be truly universal, it needs to support “live” query connections for everyone. This means a semantic layer must meet the following requirements for connecting users to data:

Multimodal : It supports a variety of use cases and personas, including the business analyst, data science and application developer.
Open : It supports a wide range of query tools using their native protocols, including SQL, MDX, DAX, Python, REST, JDBC and ODBC.
Lightweight: It has a “zero footprint” on end users machines, which means that there is no client-side software or plugins necessary to access the semantic layer.

2. Semantic Modeling

The core of the semantic layer is the data model. A business-friendly data layer cannot exist without a map of the logical elements (dimensions, metrics, hierarchies, KPIs) to the physical entities of databases, tables and relationships. In order to deliver a digital twin of the business, a semantic layer must meet the following requirements:

Object-oriented: It supports reusable models and components to drive a hub and spoke analytics management style.
Smart push down to data platforms for optimal performance.
Plug & play pre-built models for known schemas (i.e. SaaS apps, 3rd party datasets)
Programmable: It supports a CI/CD compatible markup language and shares its metadata via APIs with data catalogs & other tools.

3. Data Prep Virtualization

Data transformations are a necessary requirement for a semantic layer. A semantic layer platform should support virtualized calculations for expressing business logic and be capable of generating multi-pass SQL queries to handle calculations that require different levels of granularity like ratios and weighted averages. The semantic layer engine must deliver the following capabilities:

Open: It supports transformation expressions using the native platform’s SQL dialect & MDX expressions.
Multipass: It supports the ability to perform pre-query & post-query calculations for handling calculations at different levels of granularity.
Virtualized: It supports inline transformations using direct queries without data movement or creating c.

4. Multi-dimensional Calculation Engine

The semantic layer data model must be backed by a scalable, multi-dimensional engine to express a wide range of business concepts in a variety of contexts. The semantic layer engine must deliver the following capabilities:

Cell-based: It supports matrix-style calculations (time intelligence, multi-pass, etc,) without pre-calculation using a multidimensional expression language like MDX or DAX .
Graph-based: It supports thousands of dimensions, attributes and metrics using a graph-based query planner.
Dynamic: It supports “anything by anything” queries with constraints and filters applied at query time.

5. Performance Optimization

A semantic layer must accelerate the performance of the underlying data platform in order to deliver “speed of thought” queries. Without acceleration, a semantic layer will likely be bypassed using BI tool extracts and imports, defeating the purpose of a semantic layer. As such, a semantic layer must include the following capabilities:

Autonomous: It automatically tunes and improves performance using machine-learning and user query patterns.
In Situ: It improves performance without moving data outside the native data platform or requiring a separate cluster for managing aggregates.
Adaptive: It is always learning and improving performance based on system performance and user query behaviors.

6. Analytics Governance

There’s a range of tools and products that act as a query layer to govern data access. Data governance is a core requirement for a semantic layer and thus must be a core requirement, rather than a separate layer. For a semantic layer to satisfy a wide range of data governance, it must deliver the following capabilities:

Integrated: It integrates with corporate directory services (i.e. AD, LDAP, Okta) for user identity management.
Row & Column: It applies row-level filtering & column-level masking to every query based on user, group and role-based (RBAC) data access rules..
Real-time: It enforces governance continuously and in real time at the time of query .

7. Data Integration

In modern data and analytics ecosystems, data lives in multiple silos, including on-premise, legacy data warehouses, data lakes, cloud data warehouses and SaaS applications like Salesforce. A semantic layer must be capable of accessing and modeling data across multiple sources with the following capabilities:

Data Platform Optimized : It works with a variety of data silos equally well by supporting native platform dialects and optimizations.
Virtualized & Federated: It supports blending of data across multiple data platforms and minimizes data movement with query push down. Where necessary, a semantic layer may work in conjunction with data virtualization engines that make it possible to federate large data sets that reside on physically disparate platforms.
Extensible: It supports a variety of data types including nested data structures like JSON and supports native data platform extensions.

Not As Easy as It Looks

As you can see, building a semantic layer platform is not simply a matter of defining metrics with a cool new markup language. For a semantic layer to be practical and usable, it needs to:

Be capable of expressing your most complex business constructs
Be able to perform better than your underlying data platforms
Be able to connect live to all your data platforms
Be able to connect to all your data consumption tools
Be able to govern every query at the user level
Be able to scale to everyone in your business

If any of these semantic layer requirements is missing, working groups or individual users will inevitably and necessarily create a localized data set with a localized semantic model that fits their purpose. And then the semantic layer crumbles. In other words, it’s binary — it either works 100% or it doesn’t work at all. Therein lies the challenge for anyone building a universal semantic layer from scratch. It’s not good enough to deliver an MVP that sort of works and can be enhanced as you go. The MVP is not an MVP — it just needs to work completely on day one or no one will bother using it.

More to Come…

The team at AtScale has spent more than a decade working to deliver the vision of a universal semantic layer and making it work for real, demanding customers. A universal semantic layer has become a critical component in the modern data and analytics stack. We cannot be more pleased to see our industry partners (and competitors) agree.

In the next few weeks, I will be diving into greater detail about the seven additional capabilities of a universal semantic layer. For more information on the semantic layer, download the white paper “ The Semantics of the Semantic Layer “. It dives deep on the 7 key requirements and shares a decade of experience making it work for real, demanding Enterprise customers.

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The Hype Around Semantic Layers: How Important Are Standards?

There are several reasons why the notion of semantic layers has reached the forefront of today’s data management conversations. The analyst community is championing the data fabric tenet. The data mesh and data lake house architectures are gaining traction. Data lakes are widely deployed. Even architectural-agnostic business intelligence tooling seeks to harmonize data across sources. Each […]

There are several reasons why the notion of semantic layers has reached the forefront of today’s data management conversations. The analyst community is championing the data fabric tenet . The data mesh and data lake house architectures are gaining traction. Data lakes are widely deployed. Even architectural-agnostic business intelligence tooling seeks to harmonize data across sources.

Additionally, a semantic layer must incorporate a digital asset knowledge graph for a unified description of data assets in all sources – like those feeding data lakes and data lakehouses. This catalog is especially important for identifying what data is in unstructured data sources, relational databases, streaming data, document stores, and other sources for data fabric or data mesh deployments.

Some “semantic layers” use non-standard, proprietary technologies to store metadata. This approach prevents the use of industry-wide ontologies like FIBO , (financial services), SNOMED (medical), SCONTO (supply chain), OBML (life sciences), CDM-Core (manufacturing), GoodRelations (e-commerce), or SWIM (aviation). It also complicates future data integration and reinforces vendor lock-in.

Conversely, semantic layers implemented with W3C’s Semantic Technologies are based on open-source standards that complement an organization’s existing IT infrastructure. They future-proof the enterprise, prevent vendor lock-in, and provide a uniform view of all data (regardless of differences in formatting, types, and structure) that’s optimal for data integration, data governance, and monetization opportunities.

Semantic Layers with W3C’s Semantic Technologies

The proliferation of unstructured and semi-structured data from external sources is partly responsible for the current demands for a semantic layer. Data fabrics, data lakes, and data lakehouses contain a surplus of such data, which is useful to everyone from data scientists to BI users. Applying a standardized semantic layer atop these architectures lets end users select data through a lens of business understanding, in which data assets are described by metadata in familiar business terms.

This layer is endlessly reusable for any use case, from building machine learning models to devising applications or running analytics. Standardized semantic technologies provide a semantic layer via RDF knowledge graphs which contain standardized data models, vocabularies, and taxonomies. The three main elements of a standardized semantic layer include:

Digital Asset Knowledge Graph: This knowledge graph describes every data asset organizations have via the above metadata and data models. It’s an effective map of what’s in a data fabric, where it is, who owns it, and more.
Ontology of Business Concepts: This semantic data model describes the important business concepts that give meaning to the data in the digital asset catalog. It contains the terminology, metadata descriptions, taxonomies, and schema in words business users understand.
Inter-Graph Linkage: The final component links the business concepts and the digital assets. A strong link between these elements is necessary for a systemic data fabric and for making the data mesh architecture viable.

Tangible Advantages

Implementing a data fabric with standardized semantic technologies delivers a number of tangible advantages to the enterprise for the long-term reuse of data. Firstly, organizations will have a uniform view of all the data in their data sources. With the proper curation, this information is invaluable for selecting the best sources for analytics or for loading applications. This approach also disambiguates entities across silos, thereby eliminating silo culture and accelerating data integration.

For example, instead of having a different identifier for the same person in each database, there’s now one Universal Resource Identifier (URI) , which is critical for governing data at scale. This characteristic also makes it easier to link data to all datasets in your company, which improves things like regulatory compliance, cross-selling, upselling, and more. Additionally, such a semantic layer makes it quicker and painless to create multiple knowledge graphs for different domains and integrate them—which is critical for data mesh or data fabric deployments.

Proprietary Semantic Layers

A semantic layer without standardized W3C Semantic Technology is predicated on proprietary data formats and technologies. These technologies are primed for vendor lock-in and limiting how organizations can utilize their data. Although they describe data aspects in business terms, they don’t include a digital asset knowledge graph. Without this characteristic, there are still myriad silos across organizations.

Organizations would also have to recreate industry-specific ontologies in their vendor’s proprietary format to use them, which is an unnecessary cost and time sink. But, since a standardized semantic layer with semantic technologies involves universal RDF standards , it’s immediately applicable to industry-wide ontologies. Companies can use any technology or data format with this semantic layer, which will naturally evolve to blend with the RDF standard and the specific data models these knowledge graphs contain.

Business Meaning

There are rare cases where a proprietary semantic layer may work and the organization might not mind getting locked into the ecosystem of a vendor for their metadata management needs. But for the majority of use cases, the best way to future-proof the enterprise is to adopt a standardized semantic layer with semantic technologies. This method provides a seamless business understanding of data that complements any current or future IT needs, while reinforcing data integration, analytics, and data governance.

Presentation Layer in OSI model

Prerequisite : OSI Model

Introduction : Presentation Layer is the 6th layer in the Open System Interconnection (OSI) model. This layer is also known as Translation layer, as this layer serves as a data translator for the network. The data which this layer receives from the Application Layer is extracted and manipulated here as per the required format to transmit over the network. The main responsibility of this layer is to provide or define the data format and encryption. The presentation layer is also called as Syntax layer since it is responsible for maintaining the proper syntax of the data which it either receives or transmits to other layer(s).

Functions of Presentation Layer :

The presentation layer, being the 6th layer in the OSI model, performs several types of functions, which are described below-

Presentation layer format and encrypts data to be sent across the network.
This layer takes care that the data is sent in such a way that the receiver will understand the information (data) and will be able to use the data efficiently and effectively.
This layer manages the abstract data structures and allows high-level data structures (example- banking records), which are to be defined or exchanged.
This layer carries out the encryption at the transmitter and decryption at the receiver.
This layer carries out data compression to reduce the bandwidth of the data to be transmitted (the primary goal of data compression is to reduce the number of bits which is to be transmitted).
This layer is responsible for interoperability (ability of computers to exchange and make use of information) between encoding methods as different computers use different encoding methods.
This layer basically deals with the presentation part of the data.
Presentation layer, carries out the data compression (number of bits reduction while transmission), which in return improves the data throughput.
This layer also deals with the issues of string representation.
The presentation layer is also responsible for integrating all the formats into a standardized format for efficient and effective communication.
This layer encodes the message from the user-dependent format to the common format and vice-versa for communication between dissimilar systems.
This layer deals with the syntax and semantics of the messages.
This layer also ensures that the messages which are to be presented to the upper as well as the lower layer should be standardized as well as in an accurate format too.
Presentation layer is also responsible for translation, formatting, and delivery of information for processing or display.
This layer also performs serialization (process of translating a data structure or an object into a format that can be stored or transmitted easily).

Features of Presentation Layer in the OSI model: Presentation layer, being the 6th layer in the OSI model, plays a vital role while communication is taking place between two devices in a network.

List of features which are provided by the presentation layer are:

Presentation layer could apply certain sophisticated compression techniques, so fewer bytes of data are required to represent the information when it is sent over the network.
If two or more devices are communicating over an encrypted connection, then this presentation layer is responsible for adding encryption on the sender’s end as well as the decoding the encryption on the receiver’s end so that it can represent the application layer with unencrypted, readable data.
This layer formats and encrypts data to be sent over a network, providing freedom from compatibility problems.
This presentation layer also negotiates the Transfer Syntax.
This presentation layer is also responsible for compressing data it receives from the application layer before delivering it to the session layer (which is the 5th layer in the OSI model) and thus improves the speed as well as the efficiency of communication by minimizing the amount of the data to be transferred.

Working of Presentation Layer in the OSI model : Presentation layer in the OSI model, as a translator, converts the data sent by the application layer of the transmitting node into an acceptable and compatible data format based on the applicable network protocol and architecture. Upon arrival at the receiving computer, the presentation layer translates data into an acceptable format usable by the application layer. Basically, in other words, this layer takes care of any issues occurring when transmitted data must be viewed in a format different from the original format. Being the functional part of the OSI mode, the presentation layer performs a multitude (large number of) data conversion algorithms and character translation functions. Mainly, this layer is responsible for managing two network characteristics: protocol (set of rules) and architecture.

Presentation Layer Protocols : Presentation layer being the 6th layer, but the most important layer in the OSI model performs several types of functionalities, which makes sure that data which is being transferred or received should be accurate or clear to all the devices which are there in a closed network. Presentation Layer, for performing translations or other specified functions, needs to use certain protocols which are defined below –

Apple Filing Protocol (AFP): Apple Filing Protocol is the proprietary network protocol (communications protocol) that offers services to macOS or the classic macOS. This is basically the network file control protocol specifically designed for Mac-based platforms.
Lightweight Presentation Protocol (LPP): Lightweight Presentation Protocol is that protocol which is used to provide ISO presentation services on the top of TCP/IP based protocol stacks.
NetWare Core Protocol (NCP): NetWare Core Protocol is the network protocol which is used to access file, print, directory, clock synchronization, messaging, remote command execution and other network service functions.
Network Data Representation (NDR): Network Data Representation is basically the implementation of the presentation layer in the OSI model, which provides or defines various primitive data types, constructed data types and also several types of data representations.
External Data Representation (XDR): External Data Representation (XDR) is the standard for the description and encoding of data. It is useful for transferring data between computer architectures and has been used to communicate data between very diverse machines. Converting from local representation to XDR is called encoding, whereas converting XDR into local representation is called decoding.
Secure Socket Layer (SSL): The Secure Socket Layer protocol provides security to the data that is being transferred between the web browser and the server. SSL encrypts the link between a web server and a browser, which ensures that all data passed between them remains private and free from attacks.

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The Semantics of the Semantic Layer. I co-founded AtScale to focus on the challenges of supporting a large number of data analysts working on disparate sets of data managed in a massive lake. We borrowed the term "semantic layer" from the folks at Business Objects who originally coined it in the 1990s. The term was actually over 20 years ...
The Hype Around Semantic Layers: How Important Are Standards?
The three main elements of a standardized semantic layer include: Digital Asset Knowledge Graph: This knowledge graph describes every data asset organizations have via the above metadata and data models. It's an effective map of what's in a data fabric, where it is, who owns it, and more. Ontology of Business Concepts: This semantic data ...
Presentation Layer in OSI model
Prerequisite : OSI Model. Introduction : Presentation Layer is the 6th layer in the Open System Interconnection (OSI) model. This layer is also known as Translation layer, as this layer serves as a data translator for the network. The data which this layer receives from the Application Layer is extracted and manipulated here as per the required ...
Building a Semantic Lakehouse
A semantic lakehouse provides an abstraction layer on the physical tables and provides a business-friendly view of data consumption by defining and organizing the data by different subject areas, and defining the entities, attributes and joins. All of this simplifies the data consumption by business analysts and end users.

The Rise of the Semantic Layer: Metrics On-The-Fly

What Tools Are Out There for Building a Semantic Layer?

Semantic Layer Challenges

Semantic layer vs data mart vs presentation layer

What’s the Difference to OLAP, Data Cataloging, Virtualizations, or Mesh

Data Virtualization and Federation

Data Mesh and Data Contracts

Data Catalogs

The Semantic Warehouse

Analytics API

Will the Semantic Layer Get More Adoption?

How to build a semantic layer?

How do different business applications use the semantic layer?

How do semantic layers help LLMs better interpret data?

About the Author

Table of contents

Semantic Layers 101: Everything You Need to Know to Get Started

What is a Semantic Layer?

Why implement a Semantic Layer?

What to look for in a Semantic Layer tool?

How to design an effective Semantic Layer?

What are the top challenges with Semantic Layers?

Looking Ahead: The Future of Semantic Layers

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Semantic vs Presentation Layer in Lightdash - August 2024

Defining Semantic Layer and Presentation Layer

Semantic Layer

Presentation Layer

Related Documentation

Simplifying Data Access

Enhancing Data Discovery

Bridging the Gap Between Data and Business

Semantic Layer vs Presentation Layer

Real-World Application: Lightdash and dbt Semantic Layer

Key Aspects of the Presentation Layer

Incorporating Visual Aids

Integrating Semantic and Presentation Layers

Case Study: Lightdash and dbt Semantic Layer

Comparative Case Study

Unique Insights

Organizational Impact

Future Developments

What is a Semantic Layer? (and why you need one)

What is a Semantic Layer

Why do you need a Semantic Layer?

The different Types of Semantic Layers

1. Data Store or fat semantic layer

2. Virtual or thin semantic layer

3. Hybrid semantic layer

4. Meta Semantic layer

How are semantic layers implemented?

1. Semantic layer implemented within a BI tool

Semantic Layer implemented in a Data Warehouse/Data Lakehouse Architecture

Universal Semantic Layer

The History and Future of Semantic Layers

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What is a Semantic Layer? (and why your BI stack needs one)

What is a Semantic Layer?

The Parts of a Complete, Universal Semantic Layer

Why your modern data stack needs a Semantic Layer

Data Security

Data Performance

Stack Flexibility

Time-to-value

Future Proofing

What are the use cases of a Semantic Layer?

Embedded Analytics

Semantic Layer for BI

AI & LLM-based applications

To sum things up.

Upgrade your data stack today

Why API-Based Data Access is Essential for Modern Data Management

Transform How Data is Managed and Consumed with a Universal Semantic Layer

Data is the new oil, but it must be refined