dbt

dbt (data build tool), from dbt Labs, is the standard for the "T" in modern ELT — transforming raw data already loaded into a warehouse into clean, tested, documented models ready for analytics. Instead of ad-hoc SQL scripts, analytics engineers write modular SQL (and Python) models that dbt compiles and runs in the warehouse, applying software-engineering practices — version control with Git, automated testing, dependency management, and documentation — to analytics. This "analytics engineering" workflow made dbt central to the modern data stack. dbt comes in two forms. dbt Core is the free, open-source command-line framework; dbt Cloud (and the dbt platform) adds a managed environment with an IDE, job scheduling and orchestration, CI/CD, hosting for documentation and data lineage, a semantic layer for consistent metrics, and collaboration and governance features. dbt runs on top of the warehouse (Snowflake, BigQuery, Databricks, Redshift, and others), leveraging its compute, and integrates with ingestion tools like Fivetran and BI tools downstream, forming the transformation backbone between them. dbt serves data and analytics engineering teams that want reliable, governed, testable data models. Pricing: dbt Core is free and open source; dbt Cloud offers a free Developer plan (one seat, limited model builds), a Team plan around $100 per developer seat per month, and custom Enterprise pricing (often $200–$400 per developer seat) with advanced governance and security. It competes with Coalesce, SQLMesh, Dataform, and hand-rolled pipelines, differentiating on ubiquity, testing/documentation, and the analytics-engineering community.