Recce

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Introduction

Recce gives data teams a faster, more reliable way to understand, review and ship changes. You can launch it in any dbt project in two commands; it starts locally so you can explore lineage and run queries.

To unlock diffing tools such as data comparisons and impact checks, you prepare two environments to compare, for example production and a PR branch. The lightweight recce-cloud package uploads dbt artifacts from CI to Recce Cloud. Note that dbt-core currently needs Python 3.10 to 3.12.

Data tests belong in the same pull request as the model change, just as unit tests travel with application code. Start with a few checks on the tables your dashboards and machine learning features depend on, then widen coverage as incidents show you where data breaks.

The source code is at DataRecce/recce on GitHub under the Apache-2.0 licence, with about 483 stars; the most recent commit was on 11 November 2026.

Features

1. Lineage exploration: See affected models.
2. Data diffs: Compare environments.
3. Impact checks: Understand change effects.
4. CI uploads: recce-cloud package.
5. Local first: Runs in your dbt project.

Pros

1. Free to use: Open source under the Apache-2.0 licence, so the core tool costs nothing to use.
2. Actively maintained: The repository had commits as recent as 11 November 2026.

Cons

1. Two environments needed: For full diffing.
2. Python version limits: Due to dbt-core.

Usage and Links (Example)

pip install -U recce   # inside your dbt project
recce server

Then follow the Jaffle Shop tutorial to set up a base environment for comparisons.

Resources:

• Recce website
• GitHub repository

Pricing

Recce is free and open source under the Apache-2.0 licence.

Recommended for?

1. dbt teams reviewing model changes in PRs.

Conclusion

Recce brings code-review rigour to data changes.

Pair it with dbt tests for full coverage.

On CheckOps, Recce is filed under the Release & CI gates and Integration testing stages. Related tools listed here include dbt tests, Great Expectations (GX), pgTAP, Soda Core, Pandera and Deequ. Which one fits best depends mostly on your stack, your team’s size and skills, and whether you prefer to run tools yourself or pay for a managed service. This page reflects the official website, documentation and repository as they stood on 11 November 2026.