StreamPipes

StreamPipes enables flexible modeling of stream processing pipelines by providing a graphical modeling editor on top of existing stream processing frameworks.

It leverages non-technical users to quickly define and execute processing pipelines based on an easily extensible toolbox of data sources, data processors and data sinks.

Learn more about StreamPipes at https://www.streampipes.org/

Read the full documentation at https://docs.streampipes.org

StreamPipes examples for standalone pipeline elements running directly on the JVM

This project includes examples for StreamPipes data processors and data sinks that do not use a specific runtime such as Apache Flink but run directly on the JVM in a single-host manner. These components are suitable for processing event streams with rather low frequency (e.g., up to a few thousand events per second)

Currently, the following example pipeline elements are available:

Data Processors

  • Numerical Filter: Filters sensor values based on a configurable threshold value.
  • Text Filter: Filters text-based fields by a given string value.
  • Projection: Outputs a configurable subset of the fields available in an input event stream.

Data Sinks

  • CouchDB: Stores events in an Apache CouchDB database.
  • Dashboard: Can be used to display pipeline results in the real-time dashboard of the StreamPipes UI.
  • Kafka Publisher: Publishes events to an Apache Kafka broker.
  • Notification: Can be used to generate notifications that are shown in the notification center of the StreamPipes UI.

Getting started

Currently, the StreamPipes core is available as a preview in form of ready-to-use Docker images.

It's easy to get started:

Extending StreamPipes

You can easily add your own data streams, processors or sinks.

Check our developer guide at https://docs.streampipes.org/developer_guide/introduction

Feedback

We'd love to hear your feedback! Contact us at mail.streampipes@gmail.com