Book Image

Apache Spark Quick Start Guide

By : Shrey Mehrotra, Akash Grade
Book Image

Apache Spark Quick Start Guide

By: Shrey Mehrotra, Akash Grade

Overview of this book

Apache Spark is a ?exible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases. It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis. Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications.
Table of Contents (10 chapters)

Machine learning

Machine learning is one of the advanced analytics that harness data. Machine learning is a collection of algorithms that helps people to understand data in many different ways. These algorithms can be categorized into two categories:

  • Supervised learning: Supervised-learning algorithms are some of the most commonly used machine learning algorithms. They use historical data to train a machine- learning model. These algorithms can be further categorized into classification and regression algorithms. In classification algorithms, the model is trained to predict a categorical/discrete dependent variable. One of the basic examples is predicting whether an email is spam. On the other hand, regression algorithms predict continuous variables. An example of the regression algorithm would be to predict stock prices.
  • Unsupervised learning: In unsupervised learning, no historical...