- 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
- Overview - Spark 4. 0. 0 Documentation
It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing
- 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
- Quick Start - Spark 4. 0. 0 Documentation
Where to Go from Here 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
- Downloads - Apache Spark
As new Spark releases come out for each development stream, previous ones will be archived, but they are still available at Spark release archives NOTE: Previous releases of Spark may be affected by security issues
- Examples - Apache Spark
Spark allows you to perform DataFrame operations with programmatic APIs, write SQL, perform streaming analyses, and do machine learning Spark saves you from learning multiple frameworks and patching together various libraries to perform an analysis
- 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
- Performance Tuning - Spark 4. 0. 0 Documentation
Spark offers many techniques for tuning the performance of DataFrame or SQL workloads Those techniques, broadly speaking, include caching data, altering how datasets are partitioned, selecting the optimal join strategy, and providing the optimizer with additional information it can use to build more efficient execution plans
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