Overview
- A tutorial on the Apache Spark platform written by an expert engineer and trainer using and teaching Spark
- One of the very first books on the new Apache Spark 2.1
- Apache Spark is the leading alternative to Hadoop
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
Along the way, you’ll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you’ll learn the fundamentals of Spark ML for machine learning and much more.
After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications.
What You Will Learn
- Understand Spark unified data processing platform
- Howto run Spark in Spark Shell or Databricks
- Use and manipulate RDDs
- Deal with structured data using Spark SQL through its operations and advanced functions
- Build real-time applications using Spark Structured Streaming
- Develop intelligent applications with the Spark Machine Learning library
Who This Book Is For
Programmers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.
Similar content being viewed by others
Keywords
Table of contents (8 chapters)
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Beginning Apache Spark 2
Book Subtitle: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library
Authors: Hien Luu
DOI: https://doi.org/10.1007/978-1-4842-3579-9
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Hien Luu 2018
eBook ISBN: 978-1-4842-3579-9Published: 16 August 2018
Edition Number: 1
Number of Pages: XI, 393
Number of Illustrations: 86 b/w illustrations
Topics: Big Data, Java, Data Mining and Knowledge Discovery, Open Source