Integrate data sets and data streams using the built-in StreamPipes Connect library with support for generic protocols such as HTTP, Kafka, MQTT, OPC-UA, Files or specific adapters for open data sources.
Some included adapters and integrations:
Apache Kafka, Apache Pulsar, Apache PLC4X, Robot Operating System (ROS), Mysql, 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.
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.
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.
StreamPipes
StreamPipes makes IoT data analytics accessible to everyone: Our graphical modeler allows business analysts to define processing pipelines in an ad-hoc manner, without the need for data engineering or data science experts.
