Our code and documentation is actively evolving, so many topics (those in lighter gray color) have not been covered yet.

While some formal documentation is missing, we are focusing on prividing good quality examples, tutorials, and reference documentation, so you can learn a lot from those.

Project Status Disclaimer

kamu is at the MVP stage of maturity, but have not reached a stable release yet. Before v1.0 we reserve the right to break compatibility between the releases.

Therefore, we don’t recommend using kamu in production yet. When you use it for projects, make sure to maintain your source data separately and not rely on kamu for durable long-term data storage. This way any time a new version comes out that breaks some compatibility you can simply delete your workspace and re-create it from scratch in a matter of seconds.

Note on Performance

Please be patient with current performance and resource usage. We fully realize that waiting 15s to process a few KiB of CSV isn’t great. Stream processing technologies is a relatively new area, and the data processing engines kamu uses (e.g. Apache Spark and Flink) are tailored to run in large clusters, not on a laptop. They take a lot of resources to just boot up, so the start-stop nature of kamu’s transformations is at odds with their design. We are hoping that the industry will recognize our use-case and expect to see a better support for it in future. We are committed to improving the performance significantly in the near future.

Feature Coverage

Feature ODF kamu
Root datasets ✔️ ✔️
Ingest merge strategies ✔️ ✔️
Derivative datasets ✔️ ✔️
Validation - Metadata integrity ✔️ ✔️
Validation - Data integrity
Validation - Transformation replay ✔️ ✔️
Source evolution
Schema evolution
Query migrations
Engine versioning
Engine migrations


Component Stability
Dataset on-disk layout Unstable
CLI interface Unstable
Engine interface Unstable

See also: