Fast Data Processing with Spark 2
Accelerate your data for rapid insight
3rd Edition
Published on 8. July 2025
274 pages
978-1-78588-296-8 (ISBN)
Description
Learn how to use Spark to process big data at speed and scale for sharper analytics. Put the principles into practice for faster, slicker big data projects.
Key Features
[*] A quick way to get started with Spark - and reap the rewards
[*] From analytics to engineering your big data architecture, we've got it covered
[*] Bring your Scala and Java knowledge - and put it to work on new and exciting problems
Book DescriptionWhen people want a way to process big data at speed, Spark is invariably the solution. With its ease of development (in comparison to the relative complexity of Hadoop), it's unsurprising that it's becoming popular with data analysts and engineers everywhere. Beginning with the fundamentals, we'll show you how to get set up with Spark with minimum fuss. You'll then get to grips with some simple APIs before investigating machine learning and graph processing - throughout we'll make sure you know exactly how to apply your knowledge. You will also learn how to use the Spark shell, how to load data before finding out how to build and run your own Spark applications. Discover how to manipulate your RDD and get stuck into a range of DataFrame APIs. As if that's not enough, you'll also learn some useful Machine Learning algorithms with the help of Spark MLlib and integrating Spark with R. We'll also make sure you're confident and prepared for graph processing, as you learn more about the GraphX API.What you will learn
[*] Install and set up Spark in your cluster
[*] Prototype distributed applications with Spark s interactive shell
[*] Perform data wrangling using the new DataFrame APIs
[*] Get to know the different ways to interact with Spark s distributed representation of data (RDDs)
[*] Query Spark with a SQL-like query syntax
[*] See how Spark works with big data
[*] Implement machine learning systems with highly scalable algorithms
[*] Use R, the popular statistical language, to work with Spark
[*] Apply interesting graph algorithms and graph processing with GraphX
Who this book is forThis book is for developers with little to no knowledge of Spark, but with a background in Scala/Java programming. It's recommended that you have experience in dealing and working with big data and a strong interest in data science.
Key Features
[*] A quick way to get started with Spark - and reap the rewards
[*] From analytics to engineering your big data architecture, we've got it covered
[*] Bring your Scala and Java knowledge - and put it to work on new and exciting problems
Book DescriptionWhen people want a way to process big data at speed, Spark is invariably the solution. With its ease of development (in comparison to the relative complexity of Hadoop), it's unsurprising that it's becoming popular with data analysts and engineers everywhere. Beginning with the fundamentals, we'll show you how to get set up with Spark with minimum fuss. You'll then get to grips with some simple APIs before investigating machine learning and graph processing - throughout we'll make sure you know exactly how to apply your knowledge. You will also learn how to use the Spark shell, how to load data before finding out how to build and run your own Spark applications. Discover how to manipulate your RDD and get stuck into a range of DataFrame APIs. As if that's not enough, you'll also learn some useful Machine Learning algorithms with the help of Spark MLlib and integrating Spark with R. We'll also make sure you're confident and prepared for graph processing, as you learn more about the GraphX API.What you will learn
[*] Install and set up Spark in your cluster
[*] Prototype distributed applications with Spark s interactive shell
[*] Perform data wrangling using the new DataFrame APIs
[*] Get to know the different ways to interact with Spark s distributed representation of data (RDDs)
[*] Query Spark with a SQL-like query syntax
[*] See how Spark works with big data
[*] Implement machine learning systems with highly scalable algorithms
[*] Use R, the popular statistical language, to work with Spark
[*] Apply interesting graph algorithms and graph processing with GraphX
Who this book is forThis book is for developers with little to no knowledge of Spark, but with a background in Scala/Java programming. It's recommended that you have experience in dealing and working with big data and a strong interest in data science.
All prices
More details
Edition
3rd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Revised edition
File size
32,18 MB
ISBN-13
978-1-78588-296-8 (9781785882968)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Krishna Sankar
Fast Data Processing with Spark 2
Book
10/2016
Packt Publishing
€50.90
Shipment within 3-4 weeks
Person
Sankar Krishna :
Krishna Sankar is an AI data scientist with Volvo Cars focusing on autonomous vehicles. Previously, he was the chief data scientist at blackarrow, where he was focusing on optimizing user experience via inference, intelligence, and interfaces. He was the director of a data science/bioinformatics startup and also worked as a distinguished engineer at Cisco. He has been speaking at various conferences as well as guest lecturing at the Naval Postgraduate School. His other passion is Lego Robotics. You'll find him at the St.Louis FLL World Competition as the robot design judge.Karau Holden :
Holden Karau is a software development engineer and is active in the open source. She has worked on a variety of search, classification, and distributed systems problems at IBM, Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor's of mathematics degree in computer science. Other than software, she enjoys playing with fire and hula hoops, and welding.
Krishna Sankar is an AI data scientist with Volvo Cars focusing on autonomous vehicles. Previously, he was the chief data scientist at blackarrow, where he was focusing on optimizing user experience via inference, intelligence, and interfaces. He was the director of a data science/bioinformatics startup and also worked as a distinguished engineer at Cisco. He has been speaking at various conferences as well as guest lecturing at the Naval Postgraduate School. His other passion is Lego Robotics. You'll find him at the St.Louis FLL World Competition as the robot design judge.Karau Holden :
Holden Karau is a software development engineer and is active in the open source. She has worked on a variety of search, classification, and distributed systems problems at IBM, Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor's of mathematics degree in computer science. Other than software, she enjoys playing with fire and hula hoops, and welding.
Content
Table of Contents
Installing Spark and Setting Up Your Cluster
Using the Spark Shell
Building and Running a Spark Application
Creating a SparkSession
Loading and Saving Data in Spark
Manipulating Your RDD
Spark 2.0 Concepts
Spark SQL
Foundations of Da-tasets/DataFrames - The pro-verbial workhorse for DataSci-entists
Spark with Big Data
Machine Learning with Spark ML Pipelines
Graphx
Installing Spark and Setting Up Your Cluster
Using the Spark Shell
Building and Running a Spark Application
Creating a SparkSession
Loading and Saving Data in Spark
Manipulating Your RDD
Spark 2.0 Concepts
Spark SQL
Foundations of Da-tasets/DataFrames - The pro-verbial workhorse for DataSci-entists
Spark with Big Data
Machine Learning with Spark ML Pipelines
Graphx
