Skip to main content
Industrial IoT Toolbox
Apache StreamPipes is a self-service Industrial IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams.
Easy to use
Intuitive and fully integrated web-based user interface
Quickly implement your IIoT use cases
Developer tools for Java, Python and TypeScript
Quick-start your IIoT initiative

Made for the Industrial IoT

No more application wiring

A single user interface for everything.


IIoT Connectivity

Integrate data streams using the built-in StreamPipes Connect library with support many industrial protoocls such as S7, MQTT, Modbus, OPC-UA and many other IT protocols such as Apache Kafka and Apache Pulsar.

Some included adapters and integrations:

Apache Kafka,Apache Pulsar,Apache PLC4X (e.g., S7,Robot Operating System (ROS), OPC-UA, MQTT and more.

Adapters can be easily configured right from the user interface - with an intuitive configuration menu.

Pre-processing rules can be added to harmonize data before ingestion, e.g., transformation of measurement units.

Learn more
StreamPipes Connect OPC-UA Browser
StreamPipes Connect
StreamPipes Connect Schema Editor
StreamPipes Connect OPC-UA Browser
StreamPipes Connect
StreamPipes Connect Schema Editor



Harmonize and analyze data by using the real-time algorithm toolbox ranging from simple filters up to pre-trained neural networks - or build your own algorithm with the provided SDK.

Some included data processors:

Trend Detection, Peak Detection, Numerical Filter, Sequence, Boilerplate Removal, Event Rate, Field Converter, Frequency Calculation, Generic image Classification, Measurement Unit Converter, Projection, Timestamp Enricher, Trigonometry Functions and many more.

Our pipeline elements focus on analyzing industrial IoT data - for instance, we provide many operators to transform process data from PLC systems.

Learn more
StreamPipes Pipeline Editor
Pipeline Editor Configuration
StreamPipes Pipeline Editor
Pipeline Editor Configuration



Trigger notifications, configure your real-time dashboard or send data to third-party systems such as databases (e.g., Kafka or Elasticsearch), external services (e.g., Slack) or IoT actuators.

Some included data sinks:

Apache Kafka,Apache Pulsar,Apache CouchDB,Apache IoTDB, OPC-UA, RabbitMQ, Email, Slack, Internal Notification, PostgreSQL and more.

The brand-new data explorer gives you an intuitive and feature-rich component to visually analyze persisted time-series data and comes with ready-to-use visualizations such as heatmaps, value distribution charts or time-series charts.

Use the live dashboard to visualize data in real-time, e.g., show critical values directly on the shopfloor.

Data Explorer Live Dashboard
Data Explorer Widgets
StreamPipes Data Explorer Time Series Chart
Live Dashboard
Data Explorer Widgets
StreamPipes Data Explorer Time Series Chart
Live Dashboard

Other features

Ready for production. Out of the box.

User Management

User management is included and can be configured directly from the user interface.

StreamPipes supports the management of users, groups and permissions, so that access to views can be individually restricted.

User Management
Mail Configuration

Email & notifications

StreamPipes can be configured to send emails, e.g., as notifications directly from the pipeline editor.

With configured email settings, user self-registration and password recovery can be activated.

Container-based deployment

Besides the official source code releases, Apache StreamPipes offers ready-to-use deployment packages.

Several Docker Compose files are available to start StreamPipes with one of the supported message brokers for local setups.

In addition, helm charts are provided to deploy StreamPipes to Kubernetes clusters.

Online ML
Easy to customize & extend

First-class developer support

Apache StreamPipes is a great platform for developers: Implement custom adapters, data processors or sinks and install them at runtime.
Use StreamPipes Functions to define processing logic based on real-time IIoT data.
Or use the client libraries, available in Java and Python, to interact with live and historical data in an easy way.

Add your own extensions with the Software Development Kit

It is easy to extend StreamPipes. Whether you need connectivity to a proprietary data source, want to implement your custom-tailored algorithm as a pipeline element or need a new interface to your third party system: Simply use the SDK to extend the functionality of StreamPipes.

With its microservice architecture at its core, you can install your extensions at any time without the need to restart the whole system.

Online ML
Online ML

Interact with StreamPipes through our client libraries

StreamPipes includes Java and Python libraries which allow you to interact with StreamPipes programmatically.

You can modify the pipeline lifecycle, receive live data from all connected sources in a unified API, and Data Scientists love the possibility to extract historical data directly into Pandas data frames for in-depth analysis.

And of course, you can also just use the provided REST interface!

Seamlessly integrate AI & Machine Learning

Our Python client includes an integration with the OnlineML library River, so that you can get started with your custom anomaly detection and other ML features with just a few lines of code.

But you can also integrate other ML models, and play back the results in form of a new data stream to StreamPipes.

Online ML
Online ML

Customized User Interfaces

As a software platform that targets the Industrial IoT, we know that many applications require their own user interface, for instance, to assist maintenance personnel or to visualize machine behaviour.

The default user interface of StreamPipes can be extended with additional views by an integrated microfrontend framework.

A Typescript client library and an API to access platform features help you to build your custom IIoT solution with much less programming effort.