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)

How do we predict customer churn with Spark?


Predicting customer churn in Apache Spark is similar to predicting any other binary outcome. Spark provides a number of algorithms to do such a prediction. While we'll focus on Random Forest, you can potentially look at other algorithms within the MLLib library to perform the prediction. We'll follow the typical steps of building a machine learning pipeline that we had discussed in our earlier MLLib chapter.

The typical stages include:

  • Stage 1: Loading data/defining schema
  • Stage 2: Exploring/visualizing the data set
  • Stage 3: Performing necessary transformations
  • Stage 4: Feature engineering
  • Stage 5: Model training
  • Stage 6: Model evaluation
  • Stage 7: Model monitoring

Data set description

Since we are going to target the telecom industry, we'll use one of the popular data sets around generally used for telecommunication demonstrations. It was originally published in Discovering Knowledge in Data (http://www.dataminingconsultant.com/DKD.htm) (Larose, 2004)....