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Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.
Download Spark: spark-3.5.3-bin-hadoop3.tgz. Verify this release using the 3.5.3 signatures, checksums and project release KEYS by following these procedures. Note that Spark 3 is pre-built with Scala 2.12 in general and Spark 3.2+ provides additional pre-built distribution with Scala 2.13.
This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark’s interactive shell (in Python or Scala), then show how to write applications in Java, Scala, and Python. To follow along with this guide, first, download a packaged release of Spark from the Spark website.
The documentation linked to above covers getting started with Spark, as well the built-in components MLlib, Spark Streaming, and GraphX. In addition, this page lists other resources for learning Spark.
Seamlessly mix SQL queries with Spark programs. Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. Usable in Java, Scala, Python and R.
This page shows you how to use different Apache Spark APIs with simple examples. Spark is a great engine for small and large datasets. It can be used with single-node/localhost environments, or distributed clusters. Spark’s expansive API, excellent performance, and flexibility make it a good option for many analyses.
PySpark is the Python API for Apache Spark. It enables you to perform real-time, large-scale data processing in a distributed environment using Python. It also provides a PySpark shell for interactively analyzing your data.
Access data in HDFS, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources. MLlib is Apache Spark's scalable machine learning library, with APIs in Java, Scala, Python, and R.
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat.