Spark docker images are available from Dockerhub under the accounts of both The Apache Software Foundation and Official Images. Note that, these images contain non-ASF software and may be …

Spark Declarative Pipelines (SDP) is a declarative framework for building reliable, maintainable, and testable data pipelines on Spark. SDP simplifies ETL development by allowing you to focus on the …

DataFrame and Spark SQL share the same execution engine so they can be interchangeably used seamlessly. For example, you can register the DataFrame as a table and run a SQL easily as below:

Spark 3.5.5 released We are happy to announce the availability of Spark 3.5.5! Visit the release notes to read about the new features, or download the release today. Spark News Archive

[SPARK-53953]: Bump Avro 1.11.5 [SPARK-54649]: Upgrade Jersey to 2.47 [SPARK-54900]: Upgrade ORC to 1.9.8 You can consult JIRA for the detailed changes. We would like to acknowledge all …

Spark provides three locations to configure the system: Spark properties control most application parameters and can be set by using a SparkConf object, or through Java system properties. …

Apache Spark leverages GitHub Actions that enables continuous integration and a wide range of automation. Apache Spark repository provides several GitHub Actions workflows for developers to …

Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only “added” to, such as …

Structured Streaming Programming Guide As of Spark 4.0.0, the Structured Streaming Programming Guide has been broken apart into smaller, more readable pages. You can find these pages here.

Check out the guide on migrating from Spark JVM to Spark Connect to learn more about how to write code that works with Spark Connect. Also, check out how to build Spark Connect custom extensions …