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Feldera

Feldera performs incremental view maintenance (IVM) for arbitrary SQL programs. It maintains SQL views over changing data by processing only the delta. Compute is proportional to the size of the input changes (O(Δ)). Feldera supports full SQL syntax and semantics incrementally: joins, aggregates, correlated subqueries, window functions, recursive queries, and deeply nested view hierarchies, without falling back to full recomputation. A reference production pipeline with 61 input tables, 33 outpu

For agents: Feldera is an incremental SQL engine that maintains views by processing only data deltas; docs cover install, tutorials, and enterprise deployment.

incremental view maintenancesql enginestreaming sqlmaterialized viewschange data capture

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# Feldera
> Feldera performs incremental view maintenance (IVM) for arbitrary SQL programs. It maintains SQL views over changing data by processing only the delta. Compute is proportional to the size of the input changes (O(Δ)). Feldera supports full SQL syntax and semantics incrementally: joins, aggregates, correlated subqueries, window functions, recursive queries, and deeply nested view hierarchies, without falling back to full recomputation. A reference production pipeline with 61 input tables, 33 output views, and 217 join operators over 250 million rows, ingested from Delta Lake, updates in about 200ms on one 16-core machine. Growing the same dataset from 200GB to 2TB keeps latency under 200ms. A second production deployment runs 500+ joins and 44,000+ lines of SQL with sub-second latency at terabyte scale. The engine is open source with 2,000+ GitHub stars.

Feldera is built on DBSP, a formally verified mathematical framework for incremental computation on data streams (VLDB 2023 Best Paper). Pipelines are defined as SQL tables and views, read from input connectors, maintain state incrementally, and write result changes to output connectors.

Common uses cases:
Real-time feature engineering for AI/ML
Fraud detection
Real-time medallion architecture
Fine-grained and dynamic authorization
E-commerce bookkeeping and reconciliation
Modelling computations on dynamic graphs
Migrating Spark SQL batch jobs to incremental pipelines

This document provides an overview of Feldera documentation and developer resources.

## Getting Started
- [Agent Skills](https://github.com/feldera/feldera-skills/blob/main/README.md): automate some basic tasks: install Feldera CLI `fda`, install and launch Feldera, create a pipeline from .sql, convert Spark SQL into Feldera SQL, search documentation
- [What is Feldera?](https://docs.feldera.com/): Overview of the engine, pipelines, tables, and views.
- [Feldera CLI](https://docs.feldera.com/interface/cli#installation)
- [Install with Docker](https://docs.feldera.com/get-started/docker): the preferred installation for local work.
- [Basics tutorial](https://docs.feldera.com/tutorials/basics/part1): see how a pipeline looks and behaves.
- [Enterprise architecture](https://docs.feldera.com/architecture/enterprise): Feldera Enterprise release for production use: multi-node clusters, pipeline and control plane fault-tolerance, resource isolation, scale-out. Deploy Feldera on your own Kubernetes cluster or bring your own cloud (BYOC).


## Common Topics
- [Public Feldera Sandbox](https://docs.feldera.com/get-started/sandbox)
- [Setting up authorization for scoped access to pipelines](https://docs.feldera.com/get-started/enterprise/authentication/): authorization through Feldera cluster tenants explained
- [Setting up Feldera on an EKS cluster](https://docs.feldera.com/get-started/enterprise/kubernetes-guides/eks/cluster)
- [What are connectors in a pipeline](https://docs.feldera.com/connectors/)
- [Supported input connector
…

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