Apache Spark vs Esper
Apache Spark
spark.apache.orgEsper
espertech.comSide by side
| Figure | Apache Spark | Esper |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 5 |
| Spec score · Stream Analytics Software | 8.2 (#7) | 8.7 (#4) |
| Spec score · Stream Processing Software | 8.2 (#7) | 8.6 (#5) |
| Founded | 2009 | Esper is documented as created in 2006, with its first public release in early 2006. |
| Integrations | The listed ecosystem includes pandas, TensorFlow, PyTorch, scikit-learn, Apache Kafka, Kubernetes, Delta Lake, and Apache Iceberg. | The feature list names CSV, JMS, HTTP, Kafka, database and socket input/output adapters, and JMX metrics exposure. |
| Purpose | Apache Spark is a unified engine for large-scale data analytics. | Esper is a language, compiler and runtime for complex event processing and streaming analytics, available for Java and .NET. |
| Support | The project directs users to mailing lists and community resources for help. | Commercial support is offered on 24x7 or 8x5 schedules through phone, email or a web portal. |
Plans
Esper
- Esper 9.0.0Free
- Esper Open SourceFree
- Esper Enterprise EditionPriced on request
- EsperHAPriced on request
- Esper supportPriced on request
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.
Esper
Esper is a language, compiler, and runtime for complex event processing and streaming analytics in Java and .NET. Its Event Processing Language extends SQL with expressions over events and time. The compiler turns EPL into bytecode that can be packaged in JAR files. Esper and NEsper embed inside Java or .NET processes rather than running as standalone servers. The in-memory runtime is described as memory-efficient and low-latency and does not require external storage or services. Adapters handle CSV, JMS, HTTP, Kafka, database, and socket input and output, with JMX metrics exposure. Queries can join event streams with web services and relational databases through SQL-query or method-invocation joins. Esper Enterprise Edition adds scale-out architecture with elastic scaling, load balancing, fault tolerance, and multi-datacenter support using Apache Kafka and Apache Zookeeper. Listed uses include business-process monitoring, financial applications such as fraud detection, network and application monitoring, and sensor networks. Esper 9.0.0 is listed under GNU GPL (GPL v2); commercial redistribution licenses are available. Production support has Bronze, Silver, and Gold levels, plus a Developer Seat for how-to questions.