Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.

Spark Connect is a client-server architecture within Apache Spark that enables remote connectivity to Spark clusters from any application. PySpark provides the client for the Spark Connect server, …

Apache Spark™ Documentation Setup instructions, programming guides, and other documentation are available for each stable version of Spark below: Spark

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

Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch …

Types of time windows Spark supports three types of time windows: tumbling (fixed), sliding and session. Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time …

Apache Spark’s ability to choose the best execution plan among many possible options is determined in part by its estimates of how many rows will be output by every node in the execution plan (read, filter, …

Since pandas API on Spark does not target 100% compatibility of both pandas and PySpark, users need to do some workaround to port their pandas and/or PySpark codes or get familiar with pandas API on …

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 …

Specify the index column in conversion from Spark DataFrame to pandas-on-Spark DataFrame Use distributed or distributed-sequence default index Handling index misalignment with distributed …