Pachyderm: Data Versioning, Data Pipelines, and Data Lineage
Pachyderm is a tool for production data pipelines. If you need to chain together data scraping, ingestion, cleaning, munging, wrangling, processing, modeling, and analysis in a sane way, then Pachyderm is for you. If you have an existing set of scripts which do this in an ad-hoc fashion and you're looking for a way to "productionize" them, Pachyderm can make this easy for you.
- Containerized: Pachyderm is built on Docker and Kubernetes. Whatever languages or libraries your pipeline needs, they can run on Pachyderm which can easily be deployed on any cloud provider or on prem.
- Version Control: Pachyderm version controls your data as it's processed. You can always ask the system how data has changed, see a diff, and, if something doesn't look right, revert.
- Provenance (aka data lineage): Pachyderm tracks where data comes from. Pachyderm keeps track of all the code and data that created a result.
- Parallelization: Pachyderm can efficiently schedule massively parallel workloads.
- Incremental Processing: Pachyderm understands how your data has changed and is smart enough to only process the new data.
You can also refer to our complete developer docs to see tutorials, check out example projects, and learn about advanced features of Pachyderm.
If you'd like to see some examples and learn about core use cases for Pachyderm:
- Use Cases
- Case Studies: Learn how General Fusion uses Pachyderm to power commercial fusion research.
Keep up to date and get Pachyderm support via:
- Follow us on Twitter.
- Join our community Slack Channel to get help from the Pachyderm team and other users.
To get started, sign the Contributor License Agreement.
You should also check out our contributing guide.
Send us PRs, we would love to see what you do! You can also check our GH issues for things labeled "noob-friendly" as a good place to start. We're sometimes bad about keeping that label up-to-date, so if you don't see any, just let us know.
Pachyderm automatically reports anonymized usage metrics. These metrics help us
understand how people are using Pachyderm and make it better. They can be
disabled by setting the env variable
false in the pachd