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A Python-first data lakehouse
Run AI models, data transformation pipelines, and real-time analytics on your data lake with a self-optimizing, serverless runtime. No infrastructure overhead—just Python.
When asked, countless companies end up telling us the same story: data scientists produced a working prototype on a Jupyter notebook, but it is unclear what happens next. As a manager, I’ll add this: I don’t really like either of them because they both silos data people even more, instead of fostering processes where software engineering best practices are followed across organizations and create inefficient team topologies with diluted ownership. We’re actively building support for shared, declarative environments scoped to folders or DAGs, so that your dependency setup can be reused across tools — notebook or not — without duplication.
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