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DirectoryData & AnalyticsStreaming & Real-Time AnalyticsApache StreamPipes

Apache StreamPipes

About Apache StreamPipes

Apache StreamPipes is a self-service Industrial IoT data platform that enables non-technical users to connect, analyze and explore IoT data streams. It includes a connectivity library supporting industrial and IT protocols. It offers a real-time algorithm toolbox for data processing. It supports sending data to notifications, dashboards or third-party systems. It is distributed as open source.

Products

  • StreamPipes Connect: A library for integrating data streams using industrial and IT protocols.
  • StreamPipes Functions: Used to define processing logic based on real-time IIoT data.

Used for

  • Transforming process data from PLC systems.
  • Visualizing time-series data with charts such as heatmaps and value distribution charts.
  • Showing critical values in real time on the shopfloor.
  • Managing assets, sites, locations and resource assignments.

Who it is for

  • The platform is intended for non-technical users who connect, analyze and explore IoT data streams.
  • It is built for industrial teams.
  • It is intended for developers who implement custom adapters, data processors or sinks.
  • It is used by data scientists for extracting historical data for analysis.

From Apache StreamPipes's own website, read 2026-10-23. How we build listings.

How to evaluate Streaming & Real-Time Analytics

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Apache StreamPipes. It comes from our Streaming Data & Event Processing buyer guide.

25%
Throughput, Latency & Durability
Sustained throughput and tail latency at your partition count, ordering and exactly-once / at-least-once delivery guarantees, replication and multi-AZ durability, retention limits, and behavior under broker failure and rebalancing
20%
Operating Model & Architecture
Self-managed vs. fully managed vs. serverless vs. bring-your-own-cloud, KRaft (post-ZooKeeper) operations, diskless / object-storage-backed options, autoscaling, partition rebalancing, and upgrade and patching burden on your team
20%
Kafka-API Compatibility & Portability
Fidelity of the Kafka protocol (consumer groups, transactions, compaction), reuse of existing clients, connectors and tooling, lock-in to a single cloud, and the realistic effort to migrate in or out
15%
Stream Processing & Ecosystem
Native or integrated processing (Flink, Kafka Streams, Spark Structured Streaming, ksqlDB), connector catalog (CDC, sinks, sources), schema registry and governance, and direct landing into Iceberg / Delta lakehouse tables
10%
Security, Governance & Compliance
Encryption in transit and at rest, mTLS / SASL / OAuth and RBAC, audit logging, private networking, data residency and BYOC data-boundary control, and SOC 2 / ISO 27001 / GDPR coverage
10%
Cost Model & Economics at Scale
Pricing unit (capacity / throughput / consumption), inter-AZ and egress charges, storage and retention cost, processing billed separately, and how the bill behaves as volume, partitions, and connectors grow

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

streampipes.apache.org
CategoryData & Analytics
SubcategoryStreaming & Real-Time Analytics
PricingOpen source

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