What is a Semantic Layer ?
In today’s data-driven world, businesses need fast and reliable way to surface insights. However, raw data is often complex, scattered across multiple sources, and difficult for non-technical users to understand.
A semantic layer acts like a translator between raw data and the people who need to use it. Think of it like a restaurant menu:
- Chefs work with raw ingredients, complex recipes, and cooking techniques in the kitchen
- Customers, however, only need to see a clear menu that presents dishes in a simple and understandable way.
Similarly, a semantic layer takes raw, complex data and organizes it into clear, reusable terms.
Instead of requiring users to write complex SQL queries or navigate multiple tables (e.g., transactions_adyen, sales_stripes), they can access intuitive predefined metrics like “Total Sales”. No matter where it is used, the semantic layer ensures consistency and accuracy, always returning the correct result.
This structure enables data analysts, data scientists, and business users to interact with data using consistent definitions and relationships without needing deep technical expertise.
Benefits of a Semantic Layer
Many organizations struggle with data inconsistency when different teams use their own SQL logic, leading to conflicting reports.
A centralized semantic layer solves this problem by maintaining a single source of truth for business logic.
Other key benefits:
Self-Service Analytics for business users
- Enables non-technical users to analyze data via drag-and-drop interfaces or natural language queries.
- Reduces reliance on data teams for ad-hoc report generation
Better security & access control
- Enforces role-based access, ensuring sensitive data is only visible to authorized users.
- Supports compliance with regulations like GDPR and CCPA
Improved performance & query optimization
- Pre-aggregates data, reducing query load
- Optimized queries run faster, improving dashboards responsiveness and scalability.
Easier integration with multiple data sources
- Consolidates data from multiple platforms into a unified data model.
The Importance of a Semantic Layer for the consistency of LLM-Driven Queries
With the rise of Large Language Models (LLMs) in business intelligence, users can now interact with data through natural language queries. For example:
- “What were our top-performing products last quarter?”
- “What is our customer churn rate?”
However, without a structured data model, LLMs may misinterpret business terms or generate incorrect SQL queries.
A well-defined semantic layer ensures consistency and accuracy, translating natural language inputs into precise, context-aware queries - minimizing errors and enhancing trust in AI-driven analytics.
Example of an "AI Query Helper" on top of a semantic layer
Types of Semantic Layer
It can be implemented at different points in the data stack:
- In the Data Warehouse/ Data Lake – using views or materialized tables
- In a Dedicated Semantic Layer Tool (Universal Semantic Layer Platform) – like AtScale, dbt or Cube
- Within the BI Tool – like LookML (Looker) or Omni Analytic’s YAML-based models
Where should you implement a Semantic Layer?
Investing at either end of the data pipeline, it is crucial to ensure that metrics don’t get lost along the way. We generally prefer to keep business data logic - data structure, data definitions and data marts - close to the business to maintain clarity and accuracy, while addressing performance and security concerns - data access, data governance - as far upstream as possible.
Andrew Searson - CDO at Shearwater
We do a lot of implementations where semantic modeling is handled by Looker and, it’s more modern cousin, Omni.
Here are some pros an cons of this approach:
Advantages of BI-Integrated Semantic Layer
1. Business logic transparency: Analysts can clearly see and audit the metrics and measures in one place. You can also immediately test changes and see how it plays out in the end products (dashboards and self-service reporting environments) without leaving the tool.
2. Optimized query performance: it's possible to cache, pre-aggregate, or push down optimized queries, reducing computing costs.For example, tools like Omni leverage DuckDB to enhance efficiency. Here’s a great article on how DuckDB complements BI.
3. Faster iteration & business user autonomy: it is much closer to the business teams and subject matter experts. There ends up being more fidelity between what the business needs and how the data model is defined. If semantic model is further upstream, it’s easier to have a disconnect between subject matter experts and data engineering teams.
4. Built-in Security, Access Control, and Governance: provide row-level security (RLS) and role-based access control (RBAC).
Challenges of a BI-Integrated Semantic Layer
1. Vendor lock-in: Harder to migrate or use multiple analytics tools.
2. Lack of centralized governance: Large organizations using different analytics tools risk inconsistent data management and metric definitions across reports.
3. Performance bottlenecks for complex transformations: some tools may struggle with large-scale data transformations with the possibility of increase compute load
For example: when you use the “Import” mode in Power BI, the data is loaded directly into Power BI’s internal in-memory engine. However, these is not a problem for Omni and Looker, since these tools are designed to offload query processing to the data warehouse, using its computational resources for processing, which helps keep performance optimal and ensures that complex queries are efficiently handled by the warehouse.
Our recommendation
A semantic layer is essential for businesses seeking consistent, accurate, and scalable analytics.
Choose the BI Tool Semantic Layer if:
✔ You want business user flexibility
✔ You work with small-to-medium datasets
✔ You want to simplify architecture, governance and access control
Choose the Data Warehouse Semantic Layer or a dedicated tool if:
✔ You require high-performance query execution and you are limited on your BI tool
✔ You require robust centralized governance, security, and version control across different analytics tools.
A well-structured BI tool semantic layer can be highly efficient for a long period, supporting the business without requiring additional infrastructure. However, we often see organizations investing in overly complex architectures that they don’t actually need.
Over-engineering the semantic layer too early can lead to unnecessary costs, operational overhead, and maintenance challenges that don’t add immediate value.
For companies looking for a powerful semantic layer BI solution, we recommend Omni Analytics, which integrates easily with DBT if you ever need to evolve the architecture in the future.
Would you like to explore how a semantic layer could benefit your organization? Contact us to dive deeper into use cases and implementation strategies.
