Calculate total hourly cookie production on each event arrival on each production line, and alert if it has gone below 5% from within last 30 minutes.
- Express logics graphically and using the state-of-the-art Siddhi Streaming SQL language.
- Correlate and analyze millions of events per second in real time (<10ms latency).
- Support streaming and complex event processing constructs such as filters, streaming aggregations, patterns, non occurrence, anomaly detection and many more.
- Out-of-the-box support for machine learning; supports streaming machine learning and serves PMML and TensorFlow-based machine learning models.
- Use Incremental Aggregation Features to run queries that span from seconds to years.
- Out-of-the-box support for industry/domain-specific extensions: such as fraud detection, GIS data monitoring, message tracing, HTTP analytics, etc.
- Build stream processing applications using the graphical drag-and-drop editor or using the simple Siddhi Streaming SQL language.
- The browser-based rich developer studio provides a time-saving development experience with graphical event flow visualisation, drag-and-drop application development, syntax highlighting, autocomplete, and inline integrated documentation.
- Test and debug stream processing applications by simulating events one by one, or with random data, or playing back events from CSV files or databases.
- A single Siddhi file compressing data collection, processing, and notification logics.
- Also includes support for development through IntelliJ Idea via the Siddhi plugin.
- The easy-to-use graphical UI helps business users to create business rules and dynamically deploy them.
- Customizable dashboards with interactive and integrated widgets give users at-a-glance views and drill-down capabilities.
- Supports widget generation for building your own analytics visualizations.
- Adept different visualization views based on user roles and access control.
- Trigger alerts and notifications.
- Out-of-the-box support for event sources and event sinks: HTTP, TCP, Kafka, JMS, MQTT, Email, File, RabbitMQ, Twitter, and Amazon SQS supporting data formats such as JSON, XML, Text, CSV, Key Value and Binary.
- Support for in-memory data storage and rich data integration via out-of-the-box store connectors for RDBMS (MSSQL, Oracle, MySQL, Maria, Postgres ), MongoDB, Hbase, Cassandra, Solr, Redis, Elasticsearch and Hazelcast.
- Integrate with REST services and clients to retrieve live analytics and stored data and to access management services.
- The ability to connect and integrate with hundreds of WSO2 Enterprise Integrator Connectors.
- Simple two-node deployment supports up to 100k events per second with high availability, zero data loss, and zero downtime.
- Distributed deployment to scale and process millions of events per second with Kafka, providing exactly-once processing with no data loss even during failures.
- Write a Single Siddhi Stream SQL Application and run it anywhere: in a single node, simple two-node deployment, or full scaled distributed deployment mode.
- Multi-data center support.
- Use the lightweight Streaming Siddhi Engine (< 2MB) to process events at the edge in devices or in Android phones.
- Out-of-the-box dashboard for monitoring stream processor deployments.