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What is Feldera?

Feldera Continuous Analytics Platform, also known as Feldera Platform, is a set of containerized services packaging a fast data computational engine for continuous analytics. Feldera Platform allows users to run continuous queries directly on data in motion, without storing the data in databases or storage systems, before querying.

With Feldera Platform, DML and DDL SQL written for popular databases can operate continuously, with few changes, on data as it continuously arrives. This site explains the concepts behind Feldera Platform and how to use it through its user interface and API.

Feldera Platform's implementation is based on a streaming algebra called "DataBase Stream Processor", DBSP.


Feldera Platform processes queries and produces output continuously. When input arrives, Feldera Platform recomputes query results and sends the changes to outputs. Feldera Platform queries are written in SQL, so users who have an existing investment in analyzing data at rest with a SQL database can use much the same code to analyze data continuously with Feldera Platform.

Incremental processing

Feldera Platform is fundamentally incremental in how it handles input, computation, and output.

For input, being incremental means that Feldera Platform processes data as it arrives. Feldera Platform does not require all of the data to be on hand before beginning computation. Unlike a database, Feldera Platform does not durably store data.

For computation, being incremental means that when new data arrives, Feldera Platform does a minimal amount of work to update query results, rather than by fully recomputing them. This speeds up processing.

For output, being incremental means that Feldera Platform outputs query results as sets of changes from previous output. This improves performance and reduces output size.


In Feldera Platform, a SQL program is a collection of SQL DDL (table and view definitions):

  • SQL table definitions with CREATE TABLE specify the format of data. Feldera Platform does not store tables, since it is not a database, so table definitions do not reserve disk space or other storage.

  • SQL view definitions with CREATE VIEW specify transformations and computations. Views may draw data from tables and from other views. Feldera Platform provides powerful SQL analysis features, including time-series operators.

A program defines a computation, but it doesn't specify the source or destination for data. Those are the province of connectors and pipelines, described below.


A connector gives Feldera Platform computation access to data. There are two classes of connectors: input connectors that connect a source of input records to Feldera Platform table, and output connectors that connect a DSBP view to a destination.

Feldera Platform includes input and output connectors for Kafka, open source event streaming software from the Apache Software Foundation. Kafka's API is widely adopted, which means that these connectors also allow Feldera Platform to work directly with RedPanda and other software that use the same API.

Feldera Platform has a plug-in connector model. This means that connectors can be written to interface with whatever data sources and sinks a user would find most convenient.


A user assembles a pipeline by attaching a program's tables to input connectors and its views to output connectors. A Feldera Platform pipeline is the top-level unit of execution. Once a Feldera Platform user assembles a pipeline from a program and connectors, they may start, stop, manage, and monitor it.


Feldera is the pioneering implementation of a new theory that unifies databases, streaming computation, and incremental view maintenance, written by the inventors of that theory. See our publications for all the details.

Feldera code is available on Github using an MIT open-source license. It consists of a Rust runtime and a SQL compiler.