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Stream Processing Frameworks

Stream processing frameworks: engines, streaming databases, Python and Rust tools, pipelines and managed services.

41 topics•8 categories•Updated October 2026

What Is Stream Processing?

6 entries

Stream processing computes over events as they arrive instead of waiting for batches: filtering, enriching, joining and aggregating unbounded streams with state and time windows. Engines such as Apache Flink and Kafka Streams do this continuously, while streaming databases keep SQL views updated incrementally. Descriptions below follow each project's or vendor's own pages and are not rankings; release facts come from GitHub.

01Streaming Analytics PlatformsStreaming platforms and engines, on this site. 02Streaming DatabasesStreaming databases, on this site. 03Apache Kafka ResourcesKafka and its ecosystem, on this site. 04Event-Driven Architecture (EDA)EDA hub, on this site. 05Publish/Subscribe (Pub/Sub) MessagingPub/sub services and brokers, on this site. 06Real-Time Data Analytics PlatformsReal-time analytics platforms, on this site.

Stream Processing Engines and Frameworks

6 entries

Distributed engines and libraries for stateful stream computation.

07Apache FlinkFramework and distributed engine for stateful computations over unbounded and bounded streams. The Flink downloads page lists 2.3.0, and the project blog announced Apache Flink Agents 0.4.0 on 30 October 2026. 08Kafka StreamsClient library for building stream processing applications on Apache Kafka, documented on the Kafka site; the Kafka downloads page lists 4.4.0 as released on 22 October 2026. 09Spark Structured StreamingSpark describes building streaming applications and pipelines with the same APIs as batch Spark; the Spark news page lists 4.3.0 as released on 30 October 2026. 10Apache BeamUnified model for batch and streaming pipelines that run on engines such as Flink, Spark and Dataflow; GitHub lists v2.77.0 (7 October 2026) as the latest release. 11Apache StormDistributed real-time computation system; GitHub lists v3.1.0 (12 September 2026) as the latest release. 12Apache SamzaFramework for stateful stream applications reading from sources such as Kafka; its repository was last updated in August 2026 and has no recent release listed, so check its activity.

SQL Stream Processing and Streaming Databases

5 entries

Systems that express stream processing as continuously updated SQL.

13ksqlDBConfluent describes ksqlDB as a database for creating stream processing applications on Apache Kafka using a few SQL statements; the repository was updated on 28 October 2026. 14MaterializeStreaming database that keeps SQL views incrementally up to date; GitHub lists v26.48.2 (30 October 2026) as the latest release. 15RisingWaveStreaming database with PostgreSQL-compatible SQL for continuous processing and materialized views; GitHub lists v3.1.0 (21 September 2026) as the latest release. 16FelderaFeldera describes incremental view maintenance for the lakehouse that keeps SQL fresh in milliseconds; those performance claims are the vendor's own. GitHub lists v0.373.0 (31 October 2026). 17Timeplus ProtonStreaming-first SQL engine from Timeplus whose Proton engine is open source; GitHub lists v3.0.32 (30 October 2026).

Python, Rust and Developer-First Frameworks

4 entries

Lighter-weight libraries and engines for application developers.

18Quix StreamsPython library for stream processing on Kafka; GitHub lists v3.28.1 (15 October 2026) as the latest release. 19BytewaxPython stream processing framework; its latest GitHub release, v0.21.1, dates from November 2024 and the bytewax.io site did not respond to automated checks, so the card links to GitHub. 20PathwayPython framework for streaming data processing; GitHub lists v0.32.1 (1 August 2026), and the pathway.com site now describes a different focus, so the card links to GitHub. 21ArroyoOpen-source SQL stream processing engine. Cloudflare announced in April 2025 that it acquired Arroyo to power Cloudflare Pipelines; GitHub lists v0.15.0 (December 2025) as the latest release.

Streaming Pipelines, CDC and Storage

5 entries

Connectors, change data capture and streaming storage used with stream processors.

22Redpanda ConnectRedpanda describes a data streaming service with hundreds of connectors and YAML-configured pipelines; GitHub lists v4.116.1 (30 October 2026) as the latest release. 23Flink CDCStreaming data integration tool for Apache Flink; GitHub lists release-3.7.0 (10 October 2026). 24Apache FlussOpen-source columnar streaming storage system with primary-key tables and Flink integration; GitHub lists v1.0.0 (22 September 2026). 25Apache PaimonLake format for building a real-time lakehouse with Flink and Spark; GitHub lists release-2.1.0 (12 October 2026). 26DecodableDecodable describes a stream processing platform powered by Apache Flink and Debezium.

Managed Stream Processing Services

5 entries

Cloud and vendor services that run the engines for you.

27Amazon Managed Service for Apache FlinkAWS describes transforming and analyzing streaming data in real time with Apache Flink and integrating with other AWS services. 28Google Cloud DataflowGoogle describes a fully managed streaming analytics service with autoscaling and real-time processing, built on Apache Beam. 29Azure Stream AnalyticsMicrosoft describes real-time analytics on streaming data from the cloud to the edge. 30Confluent Cloud for Apache FlinkServerless Apache Flink integrated with Kafka topics on Confluent Cloud; IBM completed its acquisition of Confluent on 17 March 2026. 31VervericaVerverica, founded by the original creators of Apache Flink, describes an enterprise stream processing platform; its performance and cost figures are the vendor's own.

Reference Reading

6 entries

Foundational articles on streaming systems.

32The World Beyond Batch: Streaming 101 (O'Reilly)Tyler Akidau's article introducing core streaming concepts; the O'Reilly page returned 403 to automated checks and may need a manual click. 33The Dataflow Model (VLDB)Paper behind Apache Beam and Google Dataflow; the ACM page returned 403 to automated checks and may need a manual click. 34Apache Flink documentationOfficial stable documentation for Apache Flink. 35Change Data Capture (CDC) PlatformsCapturing database changes as events. 36ETL and ELT ToolsOpen-source ETL, dbt, enterprise ETL, cloud ETL and managed ELT. 37Big Data ComputingHadoop, Spark, Flink, Kafka and big data platforms.

Frequently Asked Questions

What is stream processing?

Computing over events as they arrive rather than in batches, using operations such as filtering, joins, aggregations and windows with managed state. It supports real-time analytics, fraud detection, monitoring and event-driven applications.

What is the difference between Flink, Kafka Streams and Spark Structured Streaming?

Flink is a dedicated distributed engine with strong state and event-time handling. Kafka Streams is a library embedded in your application that reads and writes Kafka topics. Spark Structured Streaming applies Spark's batch APIs to streams and suits teams already on Spark. The best choice depends on your sources, state needs and team skills.

What is a streaming database?

A database such as Materialize or RisingWave that keeps SQL views incrementally up to date as data arrives, so queries return fresh results without rerunning batch jobs. See the Streaming Databases page.

What is event time versus processing time?

Event time is when an event happened, while processing time is when the system handles it. Engines such as Flink and Beam use watermarks to handle late and out-of-order events correctly. The Streaming 101 article explains the concepts.

Should I run stream processing myself or use a managed service?

Managed options such as Amazon Managed Service for Apache Flink, Google Dataflow, Azure Stream Analytics and Confluent Cloud for Apache Flink remove cluster operations, while self-managing Flink or Spark gives control over versions and costs.

How current is this page?

Links and release facts were checked on 31 October 2026 from GitHub and project sites. The O'Reilly and ACM pages returned 403 to automated checks, and the Bytewax site did not respond. Vendor descriptions follow their own pages and are not rankings.