PySpark Cookbook

Denny Lee, Tomasz Drabas

商品描述

Combine the power of Apache Spark and Python to build effective big data applications

Key Features

  • Perform effective data processing, machine learning, and analytics using PySpark
  • Overcome challenges in developing and deploying Spark solutions using Python
  • Explore recipes for efficiently combining Python and Apache Spark to process data

Book Description

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.

You ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.

By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.

What you will learn

  • Configure a local instance of PySpark in a virtual environment
  • Install and configure Jupyter in local and multi-node environments
  • Create DataFrames from JSON and a dictionary using pyspark.sql
  • Explore regression and clustering models available in the ML module
  • Use DataFrames to transform data used for modeling
  • Connect to PubNub and perform aggregations on streams

Who This Book Is For

The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.

商品描述(中文翻譯)

結合Apache Spark和Python的強大功能,建立有效的大數據應用程式

主要特點:
- 使用PySpark進行有效的數據處理、機器學習和分析
- 克服使用Python開發和部署Spark解決方案的挑戰
- 探索有效地結合Python和Apache Spark來處理數據的方法

書籍描述:
Apache Spark是一個開源框架,用於高效的集群計算,具有強大的數據並行性和容錯性。《PySpark Cookbook》提供了有效且節省時間的食譜,以利用Python的優勢並在Spark生態系統中加以應用。

您將首先學習Apache Spark的架構,以及如何為Spark設置Python環境。然後,您將熟悉PySpark中可用的模塊,並輕鬆使用它們。除此之外,您還將了解如何使用RDD和DataFrames來抽象數據,並了解PySpark的流式處理能力。接下來,您將使用ML和MLlib來解決與PySpark的機器學習能力相關的任何問題,並使用GraphFrames來解決圖形處理問題。最後,您將探索如何使用spark-submit命令將應用程式部署到雲端。

通過閱讀本書,您將能夠使用Apache Spark的Python API來解決構建數據密集型應用程式所面臨的任何問題。

您將學到:
- 在虛擬環境中配置本地PySpark實例
- 在本地和多節點環境中安裝和配置Jupyter
- 使用pyspark.sql從JSON和字典創建DataFrames
- 探索ML模塊中可用的回歸和聚類模型
- 使用DataFrames轉換用於建模的數據
- 連接到PubNub並對流進行聚合

本書適合對Apache Spark 2.x生態系統有興趣的Python開發人員。對Python有深入的理解(並對Spark有一定的熟悉)將有助於您充分利用本書的內容。