SQLMesh/sqlmesh
Scalable and efficient data transformation framework - backwards compatible with dbt.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #561 of 2,794 repositories GStars tracks this week (top 20%).
3 releases in the last 90 days, the latest 11 days ago, with 178 contributors on GitHub.
Gained 30% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a scalable data transformation framework supporting SQL and Python with visibility and control for data teams.
- Enables efficient, incremental data transformations with virtual data environments and CI/CD for error-free deployments.
- Use for building isolated development environments to test data transformations without incurring data warehouse costs.
- Use for implementing incremental models that only reprocess changed data to optimize transformation runtime and cost.
- Use for running automated unit tests and audits on data models to ensure data quality and correctness before deployment.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
SQLMesh is a next-generation data transformation framework designed to ship data quickly, efficiently, and without error. Data teams can run and deploy data transformations written in SQL or Python with visibility and control at any size.
It is more than just a dbt alternative.
Get instant SQL impact and context of your changes, both in the CLI and in the SQLMesh VSCode Extension
For more information, check out the website and documentation.
Install SQLMesh through pypi by running:
Note: You may need to run
python3orpip3instead ofpythonorpip, depending on your python installation.Follow the quickstart guide to learn how to use SQLMesh. You already have a head start!
Follow the crash course to learn the core movesets and use the easy to reference cheat sheet.
Follow this example to learn how to use SQLMesh in a full walkthrough.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
480+66
Open issues
331+69
Compared with peers in Data Engineering
Fastest-growing repositories GStars tracks in this cohort for the week of Oct 18, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| apache/superset | 75.3K | +0 | 75 | 6 days ago |
| pathwaycom/pathway | 62.1K | +0 | 75 | 82 days ago |
| webtorrent/webtorrent | 31.4K | +0 | 75 | 86 days ago |
| influxdata/telegraf | 17.9K | +0 | 75 | 8 days ago |
| leeoniya/uPlot | 10.6K | +0 | 35 | 587 days ago |
| SQLMesh/sqlmesh | 3.3K | +0 | 75 | 11 days ago |
| Category median | 31.4K | +0 | 75 | — |
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