Splitgraph lets you work on data with your existing tools whilst also introducing concepts and best practices from the software engineering discipline.
Splitgraph's versioning is implemented on top of the SQL standard. Any existing tool can interact with a Splitgraph table and benefit from its change tracking capabilities, for example, dbt, Jupyter or Metabase
The decentralized demo shows the basics of running Git operations on data.
sgr command line client is the easiest way to manipulate data
images and manage your Splitgraph engine. It ships as a
single binary, freeing you from needing to set up a working Python environment, and takes care
of running the Splitgraph engine in Docker with
sgr engine commands.
Splitgraph is partially written in Python, letting it directly integrate with Python's vast data science ecosystem. You can manipulate Splitgraph images and repositories directly from your Python code or Jupyter notebook and export Splitgraph data directly to Pandas DataFrames, including using layered querying.
See the Jupyter/scikit-learn demo for a showcase.