Leading the incremental compute revolution
Batch jobs waste 99.9% of their time re-processing data that hasn’t changed
Feldera’s award-winning Incremental Compute platform erases that waste with instant incremental updates. Whether processing 10K line monster SQL pipelines with hundreds of joins or recursive graph analytics, process millions of changes per second even on a laptop.
We broke a 50-year old barrier in database research
Powered by an award-winning mathematical foundation, Feldera is the first engine that incrementally maintains *any* SQL over changing data. Compute deeply nested views with joins, distincts, unions, recursion, aggregates, sliding windows, and more, while input data changes (inserts, updates or deletes). Our customers have slashed time-to-insight from days and hours to seconds, and analytics costs by up to 10x.
From batch to incremental analytics
Feldera incrementally maintains even the most complex batch SQL pipelines as new data arrives. Bring your warehouse SQL, as is, no rewrites required.
Sub-second time-to-insight
No full-recomputations. Compute only on deltas, even at terabyte-scale.
Bring your existing SQL
Arbitrarily complex SQL including recursion with deeply nested views.
Unparalleled efficiency
Process millions of changes per second on a laptop, with up to 95% cloud cost savings.
Choose your adventure
Recurring batch jobs
Teams take monster SQL jobs from Spark and Snowflake to run them fully incrementally in Feldera. We’re talking single pipelines with 500+ joins, unions, and aggregates updating in under a second, eliminating data engineering drag and slashing cloud costs.
Turn your Spark SQL into live pipelines
Recursive queries and graph analytics
Feldera incrementally evaluates even the most complex recursive SQL as your graph evolves. It is used by security and observability teams building powerful dynamic authorization engines, real-time network-graph analytics, and always-fresh observability dashboards.
Run recursive graph queries instantlyVMware Skyline used the Feldera team's technology to digest terabytes of streaming data and execute thousands of complex rules instantaneously. We reduced our end-to-end recommendation notification time from twenty-four hours to minutes. The engine is ridiculously fast and was never a compute bottleneck for the three years it has been in production.
Alex Bewley
Director, Engineering - VMwareFully automatic. Always consistent. Built for change.
Fully automatic incremental compute, with strong consistency, ad-hoc queries, checkpoints, and fault-tolerance.
Ready to eliminate wasteful recomputation?
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