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Apache Spark

spark.apache.org

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Spec score 8.1 #5 of 25 in Streaming Analytics Software

What it is

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.

Spec sheet

Purpose
Apache Spark is a unified engine for large-scale data analytics.Read 30 Sep 2026 · spark.apache.org
Deployment
Spark documents standalone, Hadoop YARN, and Kubernetes deployment options.Read 30 Sep 2026 · spark.apache.org
Integrations
The listed ecosystem includes pandas, TensorFlow, PyTorch, scikit-learn, Apache Kafka, Kubernetes, Delta Lake, and Apache Iceberg.Read 30 Sep 2026 · spark.apache.org
Security
Security features such as authentication are not enabled by default, and the documentation says deployments are not secure by default.Read 30 Sep 2026 · spark.apache.org
Support
The project directs users to mailing lists and community resources for help.Read 30 Sep 2026 · spark.apache.org
License
The project website states Apache Spark is licensed under the Apache License, Version 2.0.Read 30 Sep 2026 · spark.apache.org
Founded
2009Read 28 Sep 2026 · spark.apache.org
Batch and streaming
Spark processes data in batches and real-time streams using Python, SQL, Scala, Java, or R.Read 30 Sep 2026 · spark.apache.org
Client connectivity
Spark Connect separates client applications from Spark clusters and supports remote connectivity.Read 30 Sep 2026 · spark.apache.org
Data science
The project says Spark supports exploratory data analysis on petabyte-scale data without downsampling.Read 30 Sep 2026 · spark.apache.org
Encryption
Spark supports TLS encryption for RPC connections and encryption of temporary data written to local disks.Read 30 Sep 2026 · spark.apache.org
Machine learning
MLlib provides machine-learning algorithms that can run locally and scale to clusters.Read 30 Sep 2026 · spark.apache.org
Operating systems
Spark runs on Windows and UNIX-like systems, including Linux and macOS, where a supported Java version is available.Read 30 Sep 2026 · spark.apache.org
Release and runtime requirements
The 4.2.0 documentation lists Java 17, 21, or 25, Scala 2.13, Python 3.10+, and R 4.0+ (deprecated).Read 30 Sep 2026 · spark.apache.org
SQL
Spark SQL executes distributed ANSI SQL queries and supports structured tables and unstructured data such as JSON or images.Read 30 Sep 2026 · spark.apache.org

Plans and prices

Apache Spark Free Open-source distributed data analytics engine · download, PyPI, Maven Central, and Docker options Read 30 Sep 2026 · spark.apache.org

Every Apache Spark plan and price →

Strong points and limits

Strong points

  • Processes batch data and real-time streams.
  • Distributed ANSI SQL supports structured and unstructured data.
  • MLlib algorithms can run locally or scale to clusters.
  • Runs on Windows, Linux, and macOS with supported Java.

Limits

  • Security features are not enabled by default.
  • R 4.0+ is listed as deprecated.
  • A supported Java version is required.

Who it suits

It suits teams and developers processing batch or streaming data with distributed SQL, machine learning, or supported language APIs. The documented deployment choices cover standalone, Hadoop YARN, and Kubernetes environments.

Verdict

Spark combines distributed analytics, SQL, streaming, and machine-learning capabilities across several languages and deployment options. Plan for its runtime requirements and configure security, since features are not enabled by default.

Where Specifiction places it

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