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| <h1>PySpark Documentation<a class="headerlink" href="#pyspark-documentation" title="Permalink to this headline">¶</a></h1> |
| <p><a class="reference external" href="https://mybinder.org/v2/gh/apache/spark/v3.2.1?filepath=python%2Fdocs%2Fsource%2Fgetting_started%2Fquickstart_df.ipynb">Live Notebook</a> | <a class="reference external" href="https://github.com/apache/spark">GitHub</a> | <a class="reference external" href="https://issues.apache.org/jira/projects/SPARK/issues">Issues</a> | <a class="reference external" href="https://github.com/apache/spark/tree/v3.2.1/examples/src/main/python">Examples</a> | <a class="reference external" href="https://spark.apache.org/community.html">Community</a></p> |
| <p>PySpark is an interface for Apache Spark in Python. It not only allows you to write |
| Spark applications using Python APIs, but also provides the PySpark shell for |
| interactively analyzing your data in a distributed environment. PySpark supports most |
| of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib |
| (Machine Learning) and Spark Core.</p> |
| <img alt="PySpark Components" src="_images/pyspark-components.png" /> |
| <p><strong>Spark SQL and DataFrame</strong></p> |
| <p>Spark SQL is a Spark module for structured data processing. It provides |
| a programming abstraction called DataFrame and can also act as distributed |
| SQL query engine.</p> |
| <p><strong>pandas API on Spark</strong></p> |
| <p>pandas API on Spark allows you to scale your pandas workload out. |
| With this package, you can:</p> |
| <ul class="simple"> |
| <li><p>Be immediately productive with Spark, with no learning curve, if you are already familiar with pandas.</p></li> |
| <li><p>Have a single codebase that works both with pandas (tests, smaller datasets) and with Spark (distributed datasets).</p></li> |
| <li><p>Switch to pandas API and PySpark API contexts easily without any overhead.</p></li> |
| </ul> |
| <p><strong>Streaming</strong></p> |
| <p>Running on top of Spark, the streaming feature in Apache Spark enables powerful |
| interactive and analytical applications across both streaming and historical data, |
| while inheriting Spark’s ease of use and fault tolerance characteristics.</p> |
| <p><strong>MLlib</strong></p> |
| <p>Built on top of Spark, MLlib is a scalable machine learning library that provides |
| a uniform set of high-level APIs that help users create and tune practical machine |
| learning pipelines.</p> |
| <p><strong>Spark Core</strong></p> |
| <p>Spark Core is the underlying general execution engine for the Spark platform that all |
| other functionality is built on top of. It provides an RDD (Resilient Distributed Dataset) |
| and in-memory computing capabilities.</p> |
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