Directory Data & AnalyticsStreaming & Real-Time AnalyticsApache StreamPipes

Apache StreamPipes

About Apache StreamPipes

Apache StreamPipes is an Industrial IoT platform designed to enable non-technical users to connect, analyze, and explore streaming data from diverse industrial sources. It provides an intuitive web-based interface that simplifies the integration of multiple data streams using built-in adapters supporting protocols like MQTT, OPC-UA, Apache Kafka, and more. This platform is tailored for enterprises seeking to accelerate their IIoT initiatives without deep technical expertise in data pipeline development.

The platform offers powerful real-time data processing capabilities, including pre-processing, filtering, and advanced analytics with support for custom algorithms via SDKs. Users can create complex pipelines to harmonize and analyze industrial data, leveraging pre-trained models or building their own. StreamPipes also facilitates operational insights by enabling real-time dashboards, notifications, and seamless data export to third-party systems such as databases and messaging services. Its extensible microservice architecture supports custom adapter and processor development, making it adaptable to evolving enterprise requirements.

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

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