Fast Data Processing with Spark 2

Sankar, Krishna

ISBN 10: 1785889273 / ISBN 13: 9781785889271

Published by Packt Publishing, 2016

About this Item

&Uumlber den AutorrnrnKrishna 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, intellig.

Seller Inventory # 448321811

Bibliographic Details

• Title: Fast Data Processing with Spark 2
• Publisher: Packt Publishing
• Publication Date: 2016
• Language: English
• ISBN 10: 1785889273
• ISBN 13: 9781785889271
• Binding: Soft cover
• Edition: 3rd Edition
• Condition: New

About this title

Synopsis

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 Description

When 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

About the Author

Krishna Sankar is a Senior Specialist—AI Data Scientist with Volvo Cars focusing on Autonomous Vehicles. His earlier stints include Chief Data Scientist at http://cadenttech.tv/, Principal Architect/Data Scientist at Tata America Intl. Corp., Director of Data Science at a bioinformatics startup, and as a Distinguished Engineer at Cisco. He has been speaking at various conferences including ML tutorials at Strata SJC and London 2016, Spark Summit [goo.gl/ab30lD], Strata-Spark Camp, OSCON, PyCon, and PyData, writes about Robots Rules of Order [goo.gl/5yyRv6], Big Data Analytics—Best of the Worst [goo.gl/ImWCaz], predicting NFL, Spark [http://goo.gl/E4kqMD], Data Science [http://goo.gl/9pyJMH], Machine Learning [http://goo.gl/SXF53n], Social Media Analysis [http://goo.gl/D9YpVQ] as well as has been a guest lecturer at the Naval Postgraduate School. His occasional blogs can be found at https://doubleclix.wordpress.com/. His other passion is flying drones (working towards Drone Pilot License (FAA UAS Pilot) and Lego Robotics—you will find him at the St.Louis FLL World Competition as Robots Design Judge.

Table of Contents

1. Installing Spark and Setting Up Your Cluster
2. Using the Spark Shell
3. Building and Running a Spark Application
4. Creating a SparkSession Object
5. Loading and Saving Data in Spark
6. Manipulating Your RDD
7. Spark 2.0 Concepts
8. Spark SQL
9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists
10. Spark with Big Data
11. Machine Learning with Spark ML Pipelines
12. GraphX

About the Author

Krishna Sankar is a chief data scientist at http://www.blackarrow.tv/, where he focuses on optimizing user experiences via inference, intelligence, and interfaces. His earlier roles include principal architect, data scientist at Tata America Intl, director of a data science and bioinformatics start-up, and a distinguished engineer at Cisco. He has spoken at various conferences, such as Strata-Sparkcamp, OSCON, Pycon, and Pydata about predicting NFL ( http://goo.gl/movfds), Spark ( http://goo.gl/E4kqMD), data science ( http://goo.gl/9pyJMH), machine learning ( http://goo.gl/SXF53n), and social media analysis ( http://goo.gl/D9YpVQ). He was a guest lecturer at Naval Postgraduate School, Monterey. His blogs can be found at https://doubleclix.wordpress.com/. His other passion is Lego Robotics. You can find him at the St. Louis FLL World Competition as the robots design judge.

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.