Packt – Apache Spark Tips, Tricks, & Techniques

Packt – Apache Spark Tips, Tricks, & Techniques
English | Size: 1.50 GB
Category: Tutorial

Apache Spark has been around for quite some time, but do you really know how to get the most out of Spark? This course aims at giving you new possibilities; you will explore many aspects of Spark, some you may have never heard of and some you never knew existed.

In this course you’ll learn to implement some practical and proven techniques to improve particular aspects of programming and administration in Apache Spark. You will explore 7 sections that will address different aspects of Spark via 5 specific techniques with clear instructions on how to carry out different Apache Spark tasks with hands-on experience. The techniques are demonstrated using practical examples and best practices.

By the end of this course, you will have learned some exciting tips, best practices, and techniques with Apache Spark. You will be able to perform tasks and get the best data out of your databases much faster and with ease.

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Apache-Spark-Tips-Tricks-Techniques

Style and Approach
This step-by-step and fast-paced guide will help you learn different techniques you can use to optimize your testing time, speed, and results with a practical approach, take your skills to the next level, and get you up-and-running with Spark.

Features
Speed up your Spark jobs by reducing shuffles
Leverage the Key/Value API in your big data processing to make your jobs work faster with lower network traffic

Compose Spark jobs from actions and transformations
Create highly concurrent Spark programs by leveraging immutability
Ways to avoid the most expensive operation in the Spark API-Shuffle
How to save data for further processing by picking the proper data format saved by Spark
Parallelize keyed data; learn of how to use Spark’s Key/Value API
Re-design your jobs to use reduceByKey instead of groupBy
Create robust processing pipelines by testing Apache Spark jobs
Solve repeated problems by leveraging the GraphX API

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