Go beyond batch and streaming

Fresh data. Any SQL. No recomputation.

Feldera is an incremental compute engine that maintains any SQL query on fresh data - in milliseconds, at any scale, at a fraction of the cost.

Built on DBSP theory (Best Paper, VLDB ‘23), it’s the only engine that incrementally maintains any SQL, including hundreds of joins, recursive queries, and sliding windows, with the exact same correctness guarantees as your warehouse.

Show us your hardest pipeline. We’ll prove it in 30 minutes.

Book a 30-min. demo

Trusted by leading engineering teams

Feldera’s IVM engine is best in class — it is, without any exaggeration, rock solid. It’ll blow your mind — DBSP incrementalizes ANY SQL query you throw at it — ANY query, even if you have a bazillion JOINs and a thousand GROUP BYs.

Securing AI agents at scale isn’t easy. Our customers include some of the most security-conscious organizations out there, and they expect rock-solid performance. Feldera helps us deliver on those expectations.

Feldera is a game changer for building the highest value real-time fraud and feature engineering pipelines. Easy to use (it’s SQL) and state of the art in performance.

See how incremental compute works

We help data engineering teams eliminate wasteful recomputation

Built to enable modern products

What teams actually build when data updates in milliseconds instead of hours

Real-time data pipelines

Most data pipelines recompute everything on every run, even though less than 0.1% of the data changed. Feldera runs your existing SQL incrementally, with no orchestration code and no scheduled batch jobs. What used to take a 70-node cluster now runs on 2 nodes.
Sensor Telemetry -> Feldera -> Fresh dashboards

Knowledge graphs for AI

AI products need structured context that’s always current, not hours stale. Feldera maintains your knowledge graph and views over it incrementally—when underlying data changes, only the affected nodes and edges update, in milliseconds. Your AI always queries a graph that reflects reality right now.
Lakehouse -> Feldera -> AI-ready knowledge graph

Fraud detection

Batch detection means fraud goes undetected for hours. Feldera continuously correlates live and historical signals in a single SQL pipeline -rolling windows, enrichment, complex joins - and catches fraud before the transaction clears.

Clickstream and event analytics

User clicks, transactions, sessions create unbounded streams that never stop growing. Most streaming engines choke on the SQL complexity these workloads demand. Feldera handles sliding windows, sessionization, and complex aggregations over infinite streams using finite resources, with correctness guarantees that no system can match at this speed.

User segmentation

Marketing teams need segments that update the moment a user does something, not whenever the next batch job runs. Feldera replaces hand-coded evaluation engines with declarative SQL views that update incrementally on every event. One pipeline handles both historical backfill and live streaming, with perfect multi-tenant isolation.

Fine-grained authorization

Every authorization request means an expensive traversal of a massive, constantly changing object graph. Feldera precomputes every authorization decision and incrementally updates only what’s affected when the graph changes, in milliseconds, using complex recursive SQL that no other engine can evaluate incrementally.
App -> Feldera -> Authorization index in Postgres

We love to share how we built Feldera

Read about the theory, customer results, the engineering challenges, and more…

Proof

Can Your Incremental Compute Engine Do This?

A real customer pipeline: 217 join operators, 33 output views, 250 million rows ingested from Delta Lake. After backfill, incremental updates run in ~200ms on a single machine using 15GB of RAM at steady state. Mihai (Chief Scientist/Co-Founder) shows the query plan and the numbers.

See the query plan

Batch

Implementing Batch Processes with Feldera

Side-by-side TPC-H benchmark: a traditional database slows down with every batch as data grows. Feldera stays flat, 170 seconds per batch, every batch, regardless of total data size. Ben (Chief Engineer/Co-Founder) shows the trend lines.

Read the benchmark

COST ANALYSIS

How Feldera Customers Slash Cloud Spend (10x and Beyond)

Why your compute bill scales with your data size instead of your data changes, and the math that fixes it. Includes a real case study: hundreds of thousands of lines of SQL migrated from a 70-node Spark cluster to one or two Feldera nodes, with views updating in milliseconds.

Read the cost breakdown

AI + AGENTS

Agents Aren’t Coworkers, Embed Them in Your Software

Most agent architectures poll, diff, and guess what changed. Feldera’s CDC streams give agents precise, incremental updates, so the agent reacts to exactly what changed, when it changed. Gerd (Head of Engineering/Co-Founder) on agentic design patterns and how incremental compute makes agents calmer and cheaper to run.

Architecture

Universal IVM: Incremental View Maintenance for the Modern Data Stack

Why no existing IVM system meets the bar, and how Feldera built the first complete one. Leonid (CTO/Co-Founder) walks through the architecture of a general-purpose IVM engine that works across data sources, from classic batch replacement to real-time ETL.

HOW TO

Accelerating Batch Analytics with Feldera

A four-part hands-on guide: create a Spark SQL batch job, convert it to a Feldera pipeline, hook up historical and real-time data sources, and send output to multiple destinations. The exact migration path your team would follow.

Start the tutorial

Show us your hardest pipelines

We’ll show you a faster way in 30 minutes.

Book a 30-min. demo