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.

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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