# apache/streampark

Make stream processing easier! Easy-to-use streaming application development framework and operation platform.

Repository: https://github.com/apache/streampark
Canonical: https://ross.abutalabs.com/products/streampark
Homepage: https://streampark.apache.org/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: streaming, streampark, apache, development-framework, easy-to-use, etl-pipeline, operation-platform
Last push: 2026-08-25T16:38:44+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 23, longevity 100
- inputs: {"age_days": 2655, "days_push": 8, "days_rel": 301, "gap_med": 191.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4328, forks 1089 (observed 2026-08-28T04:08:45.909682+00:00)

## What it is
Apache StreamPark is a streaming application development framework and one-stop cloud-native real-time computing platform for Apache Flink and Apache Spark. It standardizes configuration, development, deployment, monitoring, and operations for stream and batch processing jobs across multiple engines and environments.

## Use cases
- develop and deploy Apache Flink streaming jobs
- manage Spark and Flink applications from one platform
- build real-time ETL pipelines
- run streaming apps on Kubernetes or YARN
- monitor and operate Flink jobs in production
- simplify stream processing development for teams

## When to choose
- you develop or operate many Flink/Spark streaming jobs and want unified lifecycle management
- you need multi-engine, multi-version support on Standalone, YARN, or Kubernetes
- you want a self-hosted one-stop real-time computing platform with monitoring and alerting

## When to avoid
- you only need a lightweight job scheduler without stream processing
- your workloads don't use Flink or Spark
- you need a fully managed cloud service rather than self-hosted platform

## Facets
- artifact type: framework
- maturity: stable
- function: streaming, etl, workflow-automation, monitoring, alerting, deployment, developer-tools
- domain: big-data, analytics, developer-tools
- platform: jvm, self-hosted, cloud
- tags: apache-flink, apache-spark, stream-processing, real-time-computing, job-management, yarn, batch-processing, operation-platform, data-engineering, real-time, kubernetes, docker, web-server

## Member repositories
- apache/streampark (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:45.909682+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:41.380645+00:00, confidence not recorded.
  - readme: https://github.com/apache/streampark (fetched 2026-08-28T04:08:45.909682+00:00, sha f3b4cd1b576f)
  - homepage: https://streampark.apache.org/ (fetched 2026-08-29T09:10:37.799319+00:00, sha 2e04ad5f1be3)
  - site_page: https://streampark.apache.org/docs/get-started/introduction (fetched 2026-08-29T09:10:37.808631+00:00, sha 5a1d856837e3)
- Data as of 2026-08-30T08:39:29.467469+00:00.