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Feldera makes it easy to continuously incorporate new data into your analytical workflows. Just write SQL queries (however complex) over any number of batch and streaming data sources, and maintain results in your lakehouse.


Use cases

Feldera turns the following use-cases from expensive, ad-hoc data engineering projects to "just some SQL queries". Rapidly iterate and productionize in days instead of years.

  • Continuous feature extraction

    Continuously evaluate complex SQL over raw data to produce machine learning feature vectors.

  • Seamless lakehouse integration

    Maintain feature vectors in your lakehouse to easily integrate with the rest of your ML ecosystem, like Databricks and PySpark.

  • Rapidly iterate from dev to prod

    Seamlessly iterate and test hypotheses over both live and historical data with backfills, and deploy the exact same SQL to prod.


Powered by our state-of-the-art query engine, written from scratch in Rust.

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Query Batch and Streaming Data Without Compromise

Evaluate complex SQL on both batch and streaming data sources, seamlessly handling inserts, updates, and deletes.