Feldera provides an incremental compute engine that maintains any SQL query over changing data without recomputing the entire dataset. By processing only the data that changed, it keeps compute costs and latency flat even as data volumes grow dramatically. This enables real‑time analytics, fraud detection, and AI‑ready knowledge graphs using existing SQL pipelines.
Funding
$6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Modern data teams struggle to maintain real-time data consistency and feature parity between offline batch processing and online streaming data environments. Traditional methods require extensive data engineering and often result in delayed insights and feature skew, hindering the development of real-time applications and analytics.
Solution
Feldera offers an incremental compute engine that enables data teams to execute complex SQL queries across both batch and streaming data sources, ensuring real-time updates and strong consistency. The engine processes millions of events per second, facilitating rapid feature engineering and operational analytics without the need for extensive data engineering. By unifying offline and online compute, Feldera eliminates feature skew and accelerates the development of real-time AI, ML, and data-driven applications. The platform automatically handles incremental algorithms, strong consistency, storage spilling, and failure recovery, simplifying the development process.
Target Audience
Feldera targets AI, ML, and data teams that require real-time feature engineering, threat detection, and operational analytics, as well as organizations seeking to alleviate pressure on their data lakehouses or warehouses.
Features
- Unified query engine for both batch and streaming data sources, supporting inserts, updates, and deletes.
- Incremental computation engine written in Rust, ensuring high performance and memory stability.
- Automatic handling of incremental algorithms, consistency, storage management, and failure recovery.
- Ability to define tables and nested views, with automatic incremental updates upon input changes.
- Support for connecting to multiple heterogeneous data sources and destinations.