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 (12 chapters)

Why machine learning?


While we have given some examples on why you need machine learning, it might be helpful to look at some of the sample use cases of machine learning. Machine learning is used by us on a daily basis from fraud detection, banking, credit risk assessment, to predicting customer churn and sales volumes. People who are from a statistics background might say, "Hey - I have done all of that using simple statistics". The answer is that you have probably used a lot of the techniques that we will discuss in this book using a different name, as there is a huge overlap between statistics, data mining, and machine learning.

Some example use cases include:

  • Credit risk: To predict how likely is it for the borrower to meet its debt obligations under the agreed terms, financial institutions need to manage the credit risk inherent in the portfolio, in addition to the risks on individual credits or transactions.
  • Self-driving cars: They are the talk of the town, with everyone planning...