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apache / streampipes-extensions / 3d4c30cc4cd7642325531a4a0ac0fc693b879360
commit3d4c30cc4cd7642325531a4a0ac0fc693b879360[log] [tgz]
authorPhilipp Zehnder <zehnder@fzi.de>Thu Jun 28 23:35:47 2018 +0200
committerPhilipp Zehnder <zehnder@fzi.de>Thu Jun 28 23:35:47 2018 +0200
tree87aa0e8af45cff218035f8b2e9ddcfdfed9a24a9
parent1fc2533252a2c750f5cfa6daa98b554459eaee84 [diff]
Added docker files
12 files changed
tree: 87aa0e8af45cff218035f8b2e9ddcfdfed9a24a9
  1. streampipes-processors-aggregation-flink/
  2. streampipes-processors-enricher-flink/
  3. streampipes-processors-filters-jvm/
  4. streampipes-processors-pattern-detection-flink/
  5. streampipes-sinks-brokers-jvm/
  6. streampipes-sinks-databases-flink/
  7. streampipes-sinks-databases-jvm/
  8. streampipes-sinks-internal-jvm/
  9. streampipes-sources-vehicle-simulator/
  10. streampipes-sources-watertank-simulator/
  11. .gitignore
  12. Dockerfile
  13. LICENSE
  14. pom.xml
  15. README.md
README.md

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