Data engineering
Transformations written as tested, version-controlled SQL: software engineering for analytics.
dbt lets analysts write each transformation as a SELECT statement. It works out the order to run them in, tests the results (no duplicates, no unexpected blanks) and generates documentation and lineage.
Because models live in Git, changes are reviewed and deployed like code. dbt created the “analytics engineer” role and is now a standard layer in modern data stacks.
SQL files that build tables and views.
Data-quality checks that run with every build.
An automatic catalogue showing how every table is built.
Define metrics once and use them in every BI tool.
Typical ranges, not quotes. Scope, data and team size move them most.
The cloud data platform that made warehousing simple: storage and compute apart, almost no tuning.
The lakehouse platform from the creators of Apache Spark: data engineering, analytics and AI in one place.
The open-source scheduler that runs data pipelines in the right order, every time.
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