So, why is everyone so excited about the recently announced acquisition and upcoming integration of SDF, “a high-performance toolchain for SQL development”, into dbt? Does it make a difference for the Analytics Engineer who loves dbt for its simplicity? As it turns out, it does. And for a few different reasons.

First, as dbt’s use cases grew more complex, it became clear that some design choices on dbt’s inner workings hindered some of the developer experience. The most important point is that dbt is not a SQL parser. In simpler terms, it does not understand SQL.

dbt utilizes a templating language to add dbt-specific logic to the plain SQL code that users write. While I won’t go too deep on this (you can read the dbt Labs official announcement here with detailed explanations), not having a parser means that dbt could not optimize SQL code without running it on the database, for some large projects that could take dozens of minutes of wasted engineering time for each run (or hours per week for each developer). 

Additionally, it meant that data teams with complex, multi-platform architectures had to write intricate macros to handle database-specific SQL dialects. Similarly, database migrations require a lot more work than they would when the SDF parser is fully integrated into dbt.

Second, there is a competitive push here from other companies that offered a dbt-like tool with SQL parsers, so this is also a defensive move from dbt Labs. With a high-performing SQL parser, a large community of users, and rapidly growing enterprise adoption, dbt is almost unbeatable as the de facto standard in data transformation.