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, …

There are more guides shared with other languages such as Quick Start in Programming Guides at the Spark documentation. There are live notebooks where you can try PySpark out without any other step:

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, …

Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, …

ALTER TABLE Description ALTER TABLE statement changes the schema or properties of a table. RENAME ALTER TABLE RENAME TO statement changes the table name of an existing table in the …

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 …

Spark now comes packaged with a self-contained Maven installation to ease building and deployment of Spark from source located under the build/ directory. This script will automatically download and …

“Spark ML” is not an official name but occasionally used to refer to the MLlib DataFrame-based API. This is majorly due to the org.apache.spark.ml Scala package name used by the DataFrame-based …