Batch jobs waste 99.9% of their time reprocessing unchanged data
Feldera’s award-winning Incremental View Maintenance (IVM) engine erases that waste with instant incremental updates. Whether processing 10K line SQL pipelines with hundreds of joins or recursive graph analytics, process millions of changes per second even on a laptop.
Use cases
From batch to incremental analytics
Struggling with slow, expensive batch jobs that hamper your product's time-to-insight? With Feldera, you incrementally update complex analytics the moment data arrives -- powering real-time experiences at up to 30x lower infrastructure cost.
Use cases
Real-time graph computing
Tired of recomputing queries over your entire graph whenever data changes? Feldera incrementally evaluates even the most complex recursive queries --powering dynamic authorization engines, network analytics, and observability solutions in real time.
Use cases
Threat, identity and fraud platforms
Run thousands of complex queries to incrementally enrich, correlate and aggregate over multiple signals in real-time to stop bad actors. Whether ML-based or rule-based, compute with perfect offline-online parity at millions of events per second.
VMware 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 - VMwareIncremental computation has never been this easy
Go from painful data-engineering and ad-hoc solutions to “Just Writing SQL”. Define tables and deeply nested layers of views. When inputs receive changes (inserts, updates or deletes), all views are incrementally updated in an instant. Try it right away.
The state-of-the-art in Incremental Computation
Powered by our open-source compute engine, written from scratch in Rust.
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A unified query engine for offline and online compute, no compromises
Evaluate complex SQL over both batch and streaming data sources, seamlessly handling inserts, updates and deletes.
Documentation
Unmatched performance
Our users routinely see millions of events per second on a single machine, out-of-the-box, even for sophisticated pipelines with complex views computed over many streams.
Documentation
Fully automatic incremental compute, powered by award-winning research
We’ll take care of the incremental algorithms, strong consistency, spilling to fast storage, failure recovery and keeping memory stable even when computing over infinite streams.
DocumentationFeldera pipelines can connect to multiple heterogeneous data sources and destinations.
The Feldera Community
Join our growing community of users, engineers and enthusiasts to discuss incremental computation, databases, streaming, Rust, and all things data.
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