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:

  1. 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.

  2. 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.

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

  4. 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.


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 :)