Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (18 chapters)
Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface

Chapter 6. Machine Learning with Spark

We have spent a considerable amount of time understanding the architecture of Spark, RDDs, DataFrames and Dataset-based APIs, Spark SQL, and Streaming, all of which was primarily related to building the foundations of what we are going to discuss in this chapter, which is machine learning. Our focus has been on getting the data onto the Spark platform either in batch or in streaming fashion, and transforming it into the desired state.

Once you have the data in the platform, what do you do with it? You can either use it for reporting purposes, building dashboards, or letting your data scientists analyze the data to detect patterns, identify reasons for specific events, understand the behavior of customers, group them into segments to aid better decision making, or predict the future.

The power of Spark's MLLib stems from the fact that it lets you operate your algorithms over a distributed dataset, which can sometimes be its weakness too as not all algorithms...