Applications write data to persistent storage like a database. The most popular database query language is SQL which has many similar dialects. SQL is expressive and powerful for describing what data you want. What you do with that data requires a solution in the form of a data pipeline. Ideally, these analytical workflows can follow similar best practices to those handled in application code.
DBT is a transformation workflow that lets teams deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Users who know SQL can build production-grade data pipelines. In this episode, I interview Tristan Handy, CEO, and founder of DBT Labs.
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