A cloud-native streaming and complex event processing engine that executes Streaming SQL queries for real-time event processing.
Siddhi is a cloud-native streaming and complex event processing engine that executes Streaming SQL queries to capture, process, and analyze events in real time. It detects complex conditions across data streams and publishes outputs to various endpoints, enabling responsive event-driven applications.
Developers and architects building real-time data processing systems, IoT platforms, analytics pipelines, or microservices that require low-latency event handling and pattern detection.
Siddhi offers a unified engine with Streaming SQL for intuitive querying, supports multiple deployment modes (embedded, microservice, Kubernetes), and provides tooling for development, making it versatile for cloud-native and on-premises environments.
Stream Processing and Complex Event Processing Engine
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Uses SQL-like syntax for real-time event queries, making it accessible for developers familiar with SQL, as highlighted in the project description and README.
Supports embedded use in Java/Python, microservices, Docker containers, and Kubernetes, offering versatile integration options per the README overview.
Provides web-based graphical and textual tools for building and debugging streaming applications, enhancing developer productivity as mentioned in the features.
Built with core libraries like siddhi-core and query-api, allowing customization and extension to fit specific processing needs.
Primarily designed for Java and Python, which can limit ease of adoption in polyglot environments without additional wrappers or integration effort.
Requires downloading distributions and setting up tooling for full functionality, adding complexity compared to lighter-weight streaming solutions.
While SQL-based, mastering real-time event processing concepts and Siddhi's specific extensions demands time and expertise beyond basic SQL knowledge.