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

Feature engineering


Feature engineering is perhaps the most important topic in machine learning. The success and failure of a model to predict the future depends primarily on how you engineer features to get a better lift. The difference between an experienced data scientist and a novice would be their ability to engineer features from the data sets given, and this is perhaps the most difficult and time consuming aspect of machine learning. This is where the understanding of business problems is the key. Feature engineering is basically an art more than it is a science, and basically it is needed to frame the problem. So what is feature engineering?

Feature engineering is the process of transforming raw data into features that better represent the underlying business problem to the predictive models, resulting in improved model accuracy on unseen data.

Due to the importance of feature engineering, Spark provides algorithms for working with features divided into three major groups:

  • Feature...