Batch jobs waste 99.9% of their time reprocessing unchanged data
Feldera’s award-winning incremental compute platform erases that waste with instant incremental updates. Whether processing monster 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
Feldera users take monster SQL jobs from their Databricks or Snowflake instances and run them fully incrementally in Feldera. We’re talking pipelines with deeply nested views, *hundreds* of joins, unions, distincts and aggregates updating at sub-second speeds.
Use cases
Recursive queries and graph computing
Feldera users run sophisticated recursive graph queries to power real-time security and observability. What once took multi-year ad-hoc engineering are now simple Feldera pipelines, processing millions of graph updates per second per node.
Use cases
Threat, identity and fraud platforms
Feldera users incrementally enrich, correlate, and aggregate multiple signals in real time to stop bad actors. They achieve perfect offline-online parity, whether ML- or rule-based, processing 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.
Star us on GitHub
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.
Star us on GitHubJoin our Slack or Discord communities