Fast Data Processing with Spark - Second Edition, by Krishna Sankar, Holden Karau
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Fast Data Processing with Spark - Second Edition, by Krishna Sankar, Holden Karau
PDF Ebook Online Fast Data Processing with Spark - Second Edition, by Krishna Sankar, Holden Karau
Perform real-time analytics using Spark in a fast, distributed, and scalable way
About This Book
- Develop a machine learning system with Spark's MLlib and scalable algorithms
- Deploy Spark jobs to various clusters such as Mesos, EC2, Chef, YARN, EMR, and so on
- This is a step-by-step tutorial that unleashes the power of Spark and its latest features
Who This Book Is For
Fast Data Processing with Spark - Second Edition is for software developers who want to learn how to write distributed programs with Spark. It will help developers who have had problems that were too big to be dealt with on a single computer. No previous experience with distributed programming is necessary. This book assumes knowledge of either Java, Scala, or Python.
What You Will Learn
- Install and set up Spark on your cluster
- Prototype distributed applications with Spark's interactive shell
- Learn different ways to interact with Spark's distributed representation of data (RDDs)
- Query Spark with a SQL-like query syntax
- Effectively test your distributed software
- Recognize how Spark works with big data
- Implement machine learning systems with highly scalable algorithms
In Detail
Spark is a framework used for writing fast, distributed programs. Spark solves similar problems as Hadoop MapReduce does, but with a fast in-memory approach and a clean functional style API. With its ability to integrate with Hadoop and built-in tools for interactive query analysis (Spark SQL), large-scale graph processing and analysis (GraphX), and real-time analysis (Spark Streaming), it can be interactively used to quickly process and query big datasets.
Fast Data Processing with Spark - Second Edition covers how to write distributed programs with Spark. The book will guide you through every step required to write effective distributed programs from setting up your cluster and interactively exploring the API to developing analytics applications and tuning them for your purposes.
Fast Data Processing with Spark - Second Edition, by Krishna Sankar, Holden Karau- Amazon Sales Rank: #1593558 in Books
- Published on: 2015-03-31
- Released on: 2015-03-31
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .42" w x 7.50" l, .72 pounds
- Binding: Paperback
- 184 pages
About the Author
Krishna Sankar
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
Holden Karau is a software development engineer and is active in the open source sphere. She has worked on a variety of search, classification, and distributed systems problems at 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.
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Most helpful customer reviews
8 of 8 people found the following review helpful. Pretty Good, There are Better Books Out There By TxF At the time of this review (2015/06/29) I have purchased pretty much every Spark, Kafka and Hadoop (including YARN) book available on Amazon. This one is a good middle-of-the road, get up and running book for Spark. It doesn't including any streaming or graphx instruction, but most of the online resources are better and more up-to-date for these subjects anyhow. I found the OReilly Book, "Learning Spark" to be a much better book from a thoroughness and readability (grammar, phrasing) standpoint.One of the topics that was unique to this text was the brief walkthrough on how to deploy and test code; quite helpful.
2 of 2 people found the following review helpful. Review of "Fast Data Processing with Spark" (Second Edition) By PJG This is a useful and clear guide to getting started with Spark, and the book is a big improvement over the first version. A rapid overview of the basics, from installing Spark and then to gradually going through some of the engine's capabilities. Use of HBase and MLib are content that is helpful and well-explained.As with the first book, having examples in Python, Java and Scala is either very useful or a little annoying, depending upon your preference.Finally, at only 156 pages this is a very brief overview. Depending upon the experience of the reader, this may not be a problem, and anyone with sufficient background who wants a quick overview of the salient featuresof Spark before moving on to advanced topics would likley get on well with this book.
2 of 2 people found the following review helpful. Excellent Book for you to get start with Spark By Henry P Yang I really love this book. It provide me the good start to get to know Spark. It also provided the good code example for me to run Spark. In the official Apache Spark site, it didn't state clearly about the configuration and the way to build the application. This book stated the detailed configuration with the code example to build the application as the JAR. I followed the book, code example and POM.xml, I can quickly build the application and submit the job to Spark Workers. The book also helped me a lot to understand the other Spark key features such as Spark SQL and Machine Learning. I am so glad to implement the Spark as the part of Analytic Platform in my company successfully. My manager was very happy about it.
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