Workflow owner split between Databricks and Dataiku

The real split is workflow owner, not feature checklist. Databricks is built for teams that already manage large scale data pipelines and want one place to store data, run Spark jobs, write notebooks, train models, and now build AI systems close to the underlying compute. Dataiku sits a layer up, packaging data prep, modeling, governance, and app building into a GUI so analysts and domain teams can do useful work without living inside infrastructure tools.

• Databricks started from Spark and expanded outward, first simplifying cluster management and notebooks, then adding MLflow, Delta Lake, SQL, and model training. That makes it strongest where the buyer is a data platform or engineering team standardizing the core stack.
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• Dataiku bundles the pieces a business team would otherwise stitch together, ingest, prep, AutoML, visualization, and now generative AI app building. In practice, a manufacturer or bank can connect warehouse data, build a model or chatbot, and ship it through one governed interface.
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• The overlap is increasing because infrastructure vendors keep moving up the stack. Databricks is adding more native workflows and AI products, while Dataiku is broadening from ML workbench into AI apps and agents. The battleground is becoming the daily interface where enterprises build and govern AI work.
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Going forward, Databricks is likely to keep winning the technical system of record, while Dataiku keeps competing for the business facing control plane on top of that data. If AI adoption spreads deeper into non technical functions, the higher level layer becomes more valuable because ease of use and governance matter as much as raw infrastructure power.