One frequently asked question our Sales Engineers at Cube encounter is: "Why do we need a semantic layer if we already have a great data model built in dbt core or another tool?" While a solid data model is fundamental, it alone isn't sufficient for optimal data access and utilization. There are several reasons why adding a semantic layer becomes crucial.
Explicit metrics, not implied
Firstly, let's discuss consistency and governance. A data model organizes and simplifies data, making it easier to manage and query. However, it still demands that data analysts write SQL queries to access the data. This process, while effective, is susceptible to errors. Even skilled analysts can make mistakes, leading to inconsistencies and incorrect data retrieval. In a complex data ecosystem, it's easy to miss nuances in how metrics are calculated, which can lead to significant discrepancies. A semantic layer mitigates these issues by making the definitions of metrics explicit rather than implied. For instance, without a semantic layer, an analyst might mistakenly count order lines instead of considering the quantity field, producing inaccurate results that could pass a superficial quality check. Semantic layers encode these rules, ensuring that every data query follows the same logic and metrics definitions, eliminating the need for manual checks and reducing errors.
Abstraction reduces complexity
Beyond just consistency, the semantic layer offers a more intuitive interface for data retrieval. While data models require SQL proficiency to query data, semantic layers abstract this complexity away. This is particularly beneficial for engineers who might not be adept at writing SQL but are familiar with other interfaces like REST APIs. By compiling simple requests into complex, consistent SQL, semantic layers allow users to interact with data systems without needing in-depth SQL knowledge. For instance, instead of writing detailed SQL queries, a user can simply make a request for "order lines by month" or "order quantity by customer," and the semantic layer handles the intricacies. This abstraction not only speeds up development but also reduces the risk of bugs in applications that rely on data retrieval.
Complex queries in plain language
AI preparedness is another compelling reason to adopt a semantic layer. These layers provide the context benefits of a knowledge graph and add the constraints and compiler needed to execute queries. This makes it easier for AI systems to interact with and analyze data accurately. The semantic layer allows complex queries to be expressed in plain language, making it easier for AI to understand and process them. For example, SQL queries could be simplified to something like to "order lines by month" and "order quantity by customer." The semantic layer handles the complex SQL transformation, making the data easier to access and analyze for AI agents. This not only improves AI's accuracy but also makes the entire system more user-friendly.
Clearly defined user access
Security is another critical aspect where semantic layers shine. Because these layers are well-defined and minimal, they make it easier to enforce security policies. The clear and obvious entities within a semantic layer provide a perfect checkpoint for security measures, ensuring that only authorized users can access specific data. This is much harder to achieve with raw SQL queries where securing each query individually is cumbersome and error-prone.
Optimized cost and latency
Performance and cost efficiencies are significant benefits of adopting semantic layers. The consistent SQL generated by a semantic layer increases the likelihood of hitting the data warehouse cache, reducing both the cost and latency of data queries. Humans writing SQL can easily introduce slight variations in their queries for the same goal, decreasing cache hit probabilities. Moreover, semantic layers can enable sophisticated caching mechanisms, including pre-aggregation of known query patterns. These caches are more functional than typical data warehouse caches, providing higher cache hit ratios and even partial hits where only very recent data needs to be fetched. This is especially crucial for companies offering data as a product to their customers, where optimizing cost and latency is essential. Cloud data warehouse vendors, whose revenue is often tied to compute usage, have little incentive to invest in advanced caching. Hence, the semantic layer fills this gap, ensuring that your investment in a data model yields the desired performance and cost benefits.
In conclusion, while good data modeling is fundamental, stopping there would mean missing out on substantial benefits. The semantic layer offers enhanced consistency, a more intuitive interface, improved AI readiness, better security, and significant performance and cost advantages. These benefits make the semantic layer an indispensable component for any data-driven organization looking to maximize the utility and efficiency of their data assets.