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- Title: Taming Big Data with Apache Spark and Python – Hands On!
- Author: Frank Kane
- Length: 81 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2017-07-06
- ISBN-10: 1787287947
- ISBN-13: 9781787287945
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Key Features
- Understand how Spark can be distributed across computing clusters
- Develop and run Spark jobs efficiently using Python
- A hands-on tutorial with over 15 real-world examples teaching you Big Data processing with Spark
Book Description
Apache Spark has emerged as the next big thing in the Big Data domain - quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis. This book is your companion to learn Apache Spark in a hands-on manner. Start with understanding how to set up Spark on a single system or on a cluster. From analyzing large data sets using Spark RDD to developing and running effective Spark jobs quickly using Python, this course will teach you everything. Packed with over 15 interactive, fun-filled examples relevant to the real-world, the course will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease.
What you will learn
- Learn how you can identify the Big Data problems as Spark problems
- Install and run Apache Spark on your computer or on a cluster
- Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets
- Implement machine learning on Spark using the MLlib library
- Process continuos streams of data in real time using the Spark streaming module
- Perform complex network analysis using Spark's GraphX library
- Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster
Table of Contents
Chapter 1. Getting Started with Spark
Chapter 2. Spark Basics and Spark Examples
Chapter 3. Advanced Examples of Spark Programs
Chapter 4. Running Spark on a Cluster
Chapter 5. SparkSQL, DataFrames, and DataSets
Chapter 6. Other Spark Technologies and Libraries
Chapter 7. Where to Go From Here? – Learning More About Spark and Data Science
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