Stream Processing software handles the continuous ingestion and analysis of high-volume event data. These tools capture real-time signals from IoT devices, website interactions, and industrial assets to enable immediate querying and visualization. This type of software is for engineers and analysts who need to move data from sources to warehouses without delays. Self-hosting these apps ensures that sensitive event streams and telemetry remain on private infrastructure. You avoid external data processing costs while managing the exact flow of your telemetry and logs.
This page lists 6 open source tools in the Stream Processing category. The most popular are ThingsBoard, Apache Druid and Jitsu. Most use the Apache-2.0 or MIT license, and 6 offer an official Docker image.
Collect, process, visualize, and manage data from Internet of Things devices and industrial assets.
A high performance real-time analytics database designed for fast queries and high concurrency data ingestion.
Collect event data from websites and applications to stream into data warehouses and other external services.
Collect customer data from applications and websites to activate it in warehouses and business tools.
Run high‑throughput SQL pipelines for streaming analytics and AI using a single C++ binary.
Track and query large volumes of events in real time using Apache Kafka and ClickHouse.
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