Apache Spark Quick Start Guide

▶Book Description
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.

▶What You Will Learn
⦁ Learn core concepts such as RDDs, DataFrames, transformations, and more
⦁ Set up a Spark development environment
⦁ Choose the right APIs for your applications
⦁ Understand Spark's architecture and the execution ?ow of a Spark application
⦁ Explore built-in modules for SQL, streaming, ML, and graph analysis
⦁ Optimize your Spark job for better performance

▶Key Features
⦁ Learn about the core concepts and the latest developments in Apache Spark
⦁ Master writing efficient big data applications with Spark's built-in modules for SQL, Streaming, Machine Learning and Graph analysis
⦁ Get introduced to a variety of optimizations based on the actual experience

▶Who This Book Is For
If you are a big data enthusiast and love processing huge amount of data, this book is for you. If you are data engineer and looking for the best optimization techniques for your Spark applications, then you will find this book helpful. This book also helps data scientists who want to implement their machine learning algorithms in Spark. You need to have a basic understanding of any one of the programming languages such as Scala, Python or Java.

▶What this book covers
⦁ Chapter 1, Introduction to Apache Spark, provides an introduction to Spark 2.0. It provides a brief description of different Spark components, including Spark Core, Spark SQL, Spark Streaming, machine learning, and graph processing. It also discusses the advantages of Spark compared to other similar frameworks.

⦁ Chapter 2, Apache Spark Installation, provides a step-by-step guide to installing Spark on an AWS EC2 instance from scratch. It also helps you install all the prerequisites, such as Python, Java, and Scala.

⦁ Chapter 3, Spark RDD, explains Resilient Distributed Datasets (RDD) APIs, which are the heart of Apache Spark. It also discusses various transformations and actions that can be applied on an RDD.

⦁ Chapter 4, Spark DataFrame and Dataset, covers Spark's structured APIs: DataFrame and Dataset. This chapter also covers various operations that can be performed on a DataFrame or Dataset.

⦁ Chapter 5, Spark Architecture and Application Execution Flow, explains the interaction between different services involved in Spark application execution. It explains the role of worker nodes, executors, and drivers in application execution in both client and cluster mode. It also explains how Spark creates a Directed Acyclic Graph (DAG) that consists of stages and tasks.

⦁ Chapter 6, Spark SQL, discusses how Spark gracefully supports all SQL operations by providing a Spark-SQL interface and various DataFrame APIs. It also covers the seamless integration of Spark with the Hive metastore.

⦁ Chapter 7, Spark Streaming, Machine Learning, and Graph Analysis, explores different Spark APIs for working with real-time data streams, machine learning, and graphs. It explains the candidature of features based on the use case requirements.

⦁ Chapter 8, Spark Optimizations, covers different optimization techniques to improve the performance of your Spark applications. It explains how you can use resources such as executors and memory in order to better parallelize your tasks.