eBook: Download High Performance Spark Practices Optimizing ePub (KINDLE, PDF, MOBI) + Audio Version

  • File Size: 4225 KB
  • Print Length: 358 pages
  • Publisher: O'Reilly Media; 1 edition (May 25, 2017)
  • Publication Date: May 25, 2017
  • Language: English

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Overall, I thought this was a very good guide. It strikes a good balance between detailed training and depth and being a guidebook, not an training manual. It is the usual high quality that I've come to expect from O'Reilly, and I feel much more confident about my knowledge of Spark, both as a person and of the inner workings.

Most of the book is written with a concentrate on performance. There's some dialogue of statistical concepts, but the book is evidently aimed at helping the reader use Spark in a resource-efficient manner (which makes a lot of sense, given that Of curiosity comes into play when you're tackling large data sets).

Virtually all of the code examples are written in Scala. When I started out reading, my Scala capabilities were fairly restricted, but the authors do a good job of parsing and commenting on the code such that I now feel much more powerful in Scala, as well. They will do have a section that discusses using Python and Java (including JVM), but the majority of the guide is presented through Scala.

My one complaint relating to this book is that is actually a bit heavy on the code. It's possible that it's necessary, but I ended up skimming the majority of the coding examples, and it also made for some tiresome reading at times. After that again, there were several examples that I looked at closely, and having thorough examples did help me learn quite a bit of Scala., O’Reilly’s High Overall performance Spark is a must-have research book for seasoned data scientists and data engineers. The O’Reilly group of books is known for providing readers with the optimal blend of theory and hands-on practical applications. High Overall performance Spark continues with this winning approach.
The book is written assuming the reader has some experience working with Of curiosity or other streaming data processing engine. Novice users may find themselves being overwhelmed with the sophisticated concepts introduced in the book.
The book commences with an introduction to Spark and Scala, the building prevents to high-speed data running. This introduction examines the trade-offs why Scala is a better choice than Python or Java for buffering data processing. The writers then provide their perspective as to how Of curiosity fits into the big data ecosystem.
The next chapter centers on concepts that are familiar to all data scientists – an review on datasets, dataframes and Spark’s twist on organized query language. Later chapters extend this foundational knowledge with advanced concepts like Resilient Distributed Datasets (RDD), SQL joins, data changes and Machine Learning. Since with all O’Reilly books, after introducing the reader to the concepts, the writers provide us with the code snippets to be able to practice on our own.
I especially enjoyed the topics on writing test cases, troubleshooting, and discussions on some of the more common exceptions.
This book will definitely be on my office shelf. I highly recommend it for other data scientists and other data engineers., Apache spark moves along the continuum of parallel processing. “Apache Spark is a high-performance, general-purpose distributed computing system that has become the most active Apache available source project with more than 1, 000 active contributors”. The authors go on to mention “Spark enables us to process large quantities of data, beyond what can fit on a solitary machine, with a high-level, relatively easy-to-use API”.

Most people in (and out) from it will never have any contact with Spark. I need to know about it only because my job involves having at the very least a superficial knowledge of every considerable aspect of IT.

This book presumes you are already conversant with Apache Spark and need no education or hand-holding in that regard.

Rather this book’s goal is to help the reader make their Spark queries “faster, able to handle larger data sizes, and use fewer resources”. Being able to at least read Scala is highly recommended.

The entire book is loaded with detailed examples. For the everyday reader, such as myself, lacking a Spark environment to play in, there is an empty feeling – you can read the examples, study them, but not run them.

Getting read literally dozens or more programming cookbooks during the course of my career, this one feels right, but without being capable to run the examples, that’s just as an assumption. It will, however, make me wish I had some huge datasets to operate on. Maybe I can get a job with the NSA? I gamble there are a lot of Of curiosity experts there.

Jerry, much of the other learning material on spark is something like " if you want to join two RDDs you can use the following function"
this book however dedicates several chapters explaining it in detail and making the reader understand the internals and the performance implications., This book is heavily Scala centric and then for beginners the only alcohol delivery should be that you should be fairly comfortable with Scala If you wish to have a " Spark" Centered carrear. When you are in the big data / Warehouse space with Spark in the center of action, I highly recommend this book. It focuses seriously on all areas of Performance. You are able to keep this book handy as a reference guide as well.

Good job., this is not a beginner's guide, so you need some working knowledge of Scala and spark beforehand. < Learning Spark> and < Spark in Action> will lay a good foundation for this book. The target reader is spark programmer, all the content focuses about how to create high performance spark code, especially how to use the spark core and spark SQL API. there is nothing about how to administrative or configure a spark cluster. In this last mentioned area, one can try < expert Hadoop administration>. Having that said, this book have done a great job in explaining the nuances of writing spark code. strongly suggested.

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High Performance Spark Practices Optimizing
Average Rating: 4.45
Votes: 13
Reviews: 1