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https://spark.apache.org/
Apache Spark™ - Unified Engine for large-scale data analytics
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
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https://spark.apache.org/downloads.html
Downloads - Apache Spark
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 subject to different license terms.
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https://spark.apache.org/docs/latest/api/python/in…
PySpark Overview — PySpark 4.0.1 documentation - Apache Spark
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, allowing Spark to be used as a service.
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https://spark.apache.org/docs/latest/api/python/ge…
Getting Started — PySpark 4.0.1 documentation - Apache Spark
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:
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https://spark.apache.org/docs/latest/streaming/api…
Structured Streaming Programming Guide - Spark 4.0.1 Documentation
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 intervals. An input can only be bound to a single window.
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https://spark.apache.org/news/spark-3-5-5-released…
Spark 3.5.5 released - Apache 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
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https://spark.apache.org/docs/latest/streaming-pro…
Spark Streaming - Spark 4.0.1 Documentation
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, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like map, reduce, join and window.
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https://spark.apache.org/docs/latest/sql-performan…
Performance Tuning - Spark 4.0.1 Documentation
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, join, etc.).
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https://spark.apache.org/docs/latest/streaming/ind…
Structured Streaming Programming Guide - Spark 4.0.1 Documentation
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 computation on static data.
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https://spark.apache.org/docs/3.5.6/
Overview - Spark 3.5.6 Documentation
If you’d like to build Spark from source, visit Building Spark. Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS), and it should run on any platform that runs a supported version of Java.