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Overview - Spark 4. 0. 0 Documentation - Apache Spark Running Spark Client Applications Anywhere with Spark Connect Spark Connect is a new client-server architecture introduced in Spark 3 4 that decouples Spark client applications and allows remote connectivity to Spark clusters
Quick Start - Spark 4. 0. 0 Documentation - Apache Spark Unlike the earlier examples with the Spark shell, which initializes its own SparkSession, we initialize a SparkSession as part of the program To build the program, we also write a Maven pom xml file that lists Spark as a dependency Note that Spark artifacts are tagged with a Scala version
Documentation - Apache Spark 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
PySpark Overview — PySpark 4. 0. 0 documentation - Apache Spark PySpark combines Python’s learnability and ease of use with the power of Apache Spark to enable processing and analysis of data at any size for everyone familiar with Python PySpark supports all of Spark’s features such as Spark SQL, DataFrames, Structured Streaming, Machine Learning (MLlib) and Spark Core
Downloads - Apache Spark Download Spark: Verify this release using the and project release KEYS by following these procedures Note that Spark 4 is pre-built with Scala 2 13, and support for Scala 2 12 has been officially dropped Spark 3 is pre-built with Scala 2 12 in general and Spark 3 2+ provides additional pre-built distribution with Scala 2 13 Link with Spark
Examples - Apache Spark 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 This guide shows examples with the following Spark APIs: DataFrames; SQL; Structured Streaming; RDDs
Spark SQL DataFrames - Apache 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
Getting Started — PySpark 4. 0. 0 documentation - Apache Spark Quickstart: Spark Connect Launch Spark server with Spark Connect; Connect to Spark Connect server; Create DataFrame; Quickstart: Pandas API on Spark Object Creation; Missing Data; Operations; Grouping; Plotting; Getting data in out; Testing PySpark Build a PySpark Application; Testing your PySpark Application; Putting It All Together!