- 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
- 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
- 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
- Spark 4. 0. 0 released - Apache Spark
Spark 4 0 0 released We are happy to announce the availability of Spark 4 0 0! Visit the release notes to read about the new features, or download the release today Spark News Archive
- Spark SQL DataFrames | Apache Spark
Integrated 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
- 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
- Getting Started — PySpark 4. 0. 0 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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