Hands-On Big Data Analytics with PySpark: Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

✍ Scribed by Lai, Rudy;Potaczek, Bartlomiej

Publisher: Packt Publishing
Year: 2019
Tongue: English
Leaves: 182
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs

Key Features

Work with large amounts of agile data using distributed datasets and in-memory caching

Source data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3

Employ the easy-to-use PySpark API to deploy big data Analytics for production

Book Description

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.

You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and...

✦ Table of Contents

Table of ContentsInstalling Pyspark and Setting up Your Development EnvironmentGetting Your Big Data into the Spark Environment Using RDDsBig Data Cleaning and Wrangling with Spark NotebooksAggregating and Summarizing Data into Useful ReportsPowerful Exploratory Data Analysis with MLlibPutting Structure on Your Big Data with SparkSQLTransformations and ActionsImmutable DesignAvoiding Shuffle and Reducing Operational ExpensesSaving Data in the Correct FormatWorking with the Spark Key/Value APITesting Apache Spark JobsLeveraging the Spark GraphX API

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