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Using dbt to build for scale

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dbt Core v2 is here: still open source, now rebuilt for what's next

· 9 min read
Joel Labes
Joel Labes
Staff Developer Experience Advocate at dbt Labs
Grace Goheen
Grace Goheen
Product Manager, dbt Core at dbt Labs

Today, we published the first alpha release of dbt Core version 2.0, raising the baseline for what dbt users can expect from their transformation tool. As always, dbt Core's code is completely open source under the Apache 2.0 license. What makes this release significant is that dbt Core v2.0 is now built on the same foundations as the dbt Fusion engine – we've open-sourced a lot of Fusion code for the first time.

The launch of dbt Core v2.0 comes with key feature developments:

  • Significant parse time improvements, especially on the largest dbt projects
  • A tightly-defined language spec which makes it impossible to accidentally configure a desciptin instead of a description, and gives anyone integrating with the dbt ecosystem (including dbt's own development team!) a stable interface to build against.
  • New Parquet artifacts as a high-performance alternative to large JSON files that can be directly queried through DuckDB or your agent of choice
  • A completely revamped local documentation experience, powered by those new artifacts and capable of scaling to projects of arbitrary size.
  • A more streamlined way to build new adapters, powered by ADBC and the Arrow ecosystem
  • A simplified installation process that removes the need to fight with Python's virtual environments