Book Image

Learning Spark SQL

By : Aurobindo Sarkar
Book Image

Learning Spark SQL

By: Aurobindo Sarkar

Overview of this book

In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems. This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL. It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Chapter 6. Using Spark SQL in Machine Learning Applications

n this chapter, we will present typical use cases for using Spark SQL in machine learning applications. We will focus on the Spark machine learning API called spark.ml, which is the recommended solution for implementing ML workflows. The spark.ml API is built on DataFrames and provides many ready-to-use packages, including feature extractors, Transformers, selectors, and machine learning algorithms, such as classification, regression, and clustering algorithms. We will also use Apache Spark to perform exploratory data analysis (EDA), data pre-processing, feature engineering, and developing machine learning pipelines using spark.ml APIs and algorithms.

More specifically, in this chapter, you will learn the following topics:

  • Machine learning applications
  • Key components of Spark ML pipelines
  • Understand Feature engineering
  • Implementing machine learning pipelines/applications
  • Code examples using Spark MLlib