A practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark.
- 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
Apache Spark is a flexible 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 flow of a Spark application
- Explore built-in modules for SQL, streaming, ML, and graph analysis
- Optimize your Spark job for better performance
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.
Table of contents
- Title Page
- Copyright and Credits
- About Packt
- Introduction to Apache Spark
- Apache Spark Installation
- What is an RDD?
- Programming using RDDs
- Transformations and actions
- Types of RDDs
- Caching and checkpointing
- Understanding partitions
- Drawbacks of using RDDs
- Spark DataFrame and Dataset
- Spark Architecture and Application Execution Flow
- Spark SQL
- Spark Streaming, Machine Learning, and Graph Analysis
- Cluster-level optimizations
- Language choice
- Structured versus unstructured APIs
- File format choice
- RDD optimizations
- DataFrame and dataset optimizations
- Other Books You May Enjoy
- Title: Apache Spark Quick Start Guide
- Release date: January 2019
- Publisher(s): Packt Publishing
- ISBN: 9781789349108
You might also like
Debugging Apache Spark
Apache Spark is an extremely powerful general purpose distributed system that also happens to be extremely …
Apache Spark 2.x for Java Developers
Unleash the data processing and analytics capability of Apache Spark with the language of choice: Java …
Learning Apache Spark 2
Learn about the fastest-growing open source project in the world, and find out how it revolutionizes …
Building an End-to-End Batch Data Pipeline with Apache Spark
Explore Big Data architectures and the tools you can leverage to build an end-to-end data platform. …