Apache Spark vs Siddhi
Apache Spark
spark.apache.orgSiddhi
siddhi.ioSide by side
| Figure | Apache Spark | Siddhi |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Stream Analytics Software | 8.2 (#7) | 7.4 (#21) |
| Spec score · Stream Processing Software | 8.2 (#7) | 7.3 (#22) |
| Deployment | Spark documents standalone, Hadoop YARN, and Kubernetes deployment options. | Siddhi can run embedded as a Java or Python library, as a microservice on bare metal or a VM, in Docker, or on Kubernetes. |
| Integrations | The listed ecosystem includes pandas, TensorFlow, PyTorch, scikit-learn, Apache Kafka, Kubernetes, Delta Lake, and Apache Iceberg. | Supported event sources and destinations listed include Kafka, NATS, RabbitMQ, HTTP, gRPC, JMS, MQTT, Amazon SQS, Google Pub/Sub, email, files, and WebSocket. |
| License | The project website states Apache Spark is licensed under the Apache License, Version 2.0. | The Siddhi license page identifies Apache License 2.0. |
| Operating systems | Spark runs on Windows and UNIX-like systems, including Linux and macOS, where a supported Java version is available. | The download page lists MacOS and Linux as supported platforms for PySiddhi 5.1.0. |
| Security | Security features such as authentication are not enabled by default, and the documentation says deployments are not secure by default. | WSO2 asks vulnerability reports to be sent privately to its security mailing list and says initial response time will be under one hour. |
| Support | The project directs users to mailing lists and community resources for help. | The site links to community support resources including a mailing list, contribution information, and security reporting. |
Plans
In their own words
Apache Spark
Apache Spark is a free, open-source engine for large-scale data analytics. It processes batches and real-time streams using Python, SQL, Scala, Java, or R. Spark SQL runs distributed ANSI SQL queries on structured tables and unstructured data such as JSON or images; MLlib provides machine-learning algorithms for local use or cluster scale. The project also describes exploratory analysis on petabyte-scale data without downsampling. Listed ecosystem components include pandas, TensorFlow, PyTorch, scikit-learn, Apache Kafka, Kubernetes, Delta Lake, and Apache Iceberg. Deployment options include standalone, Hadoop YARN, and Kubernetes. Spark runs on Windows and UNIX-like systems, including Linux and macOS, when a supported Java version is available. The 4.2.0 documentation lists Java 17, 21, or 25, Scala 2.13, Python 3.10+, and R 4.0+ (deprecated). Spark Connect separates client applications from clusters for remote access. Security is not enabled by default; Spark supports TLS for RPC connections and encryption of temporary local-disk data. The project is licensed under Apache License, Version 2.0.