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
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.
Currently, the StreamPipes core is available as a preview in form of ready-to-use Docker images.
It's easy to get started:
- Download the
docker-compose.ymlfile from https://www.github.com/streampipes/preview-docker - Follow the installation guide at https://docs.streampipes.org/quick_start/installation
- Check the tour and build your first pipeline!
You can easily add your own data streams, processors or sinks.
Check our developer guide at https://docs.streampipes.org/developer_guide/introduction
We'd love to hear your feedback! Contact us at [email protected]