Learning Spark: Lightning-Fast Big Data Analysis

I was awaiting the Kindle version this great book, it offers an excellent introduction of Apache Spark. It is very readable, also for people like me who don't have full-time job programming expertise. I was already experimenting with Spark by reading and watching hundreds of posts, blogs and videos but still this book is of added value. Some questions will never be answered on sites like Stackoverflow, and for me personally this book has provided me at least answers on two of my published questions. I haven't started reading the MLlib section yet but I am glad that I have bought this book: Looking forward to a guided start of experimenting with MLlib and, in my case, Machine Learning. Code examples in Github. Great!

I thought this was a pretty good book, but I agree with some reviewers that the way code snippets were presented is problematic. The code examples, especially the later ones, are very hard to recreate, in part due to the fast moving release cycle of Spark, but also, due to the fact that unless you are in a big shop with lots of servers, it's going to be hard to recreate the conditions. Most importantly, however is that the examples are not self-contained and leave the reader having to infer what some of the variables are (say, from previous examples, continued implicitly). Maybe they did this for space considerations as the book is modest in size at 240 pages. Having said that, there aren't many Spark books out there and it does a good job with the writing in terms of describing the platform and maybe not as good a job with the code examples. For anyone who in the past has been involved in a roll your own distributed computing environment, Spark itself is an incredible welcome addition. I happened to like the way the Scala vs Python vs Java breakdown is presented, as some things are not available typically in Python, and it's useful to see the variations (or similarities) in how things are done in the respective languages. The Spark API itself for these languages is elegant in its solution. Particularly prominent is the length of Java code compared to Scala. Spark (written in Scala, which in turn is written in Java) can be leveraged in Scala with very few lines of code. I only played around with the platform in Scala and Python using the spark-shell in a Mac environment and could not make it work within cygwin on Windows (spark-shell seems to be not supported at the time of this writing for Windows/Cygwin). I did not exercise any of the later code examples. The introductory chapters were very good, while the chapter on Spark Streaming was difficult and hard to follow. The Spark SQL chapter was also good. I found only a couple of typos (not counting any code errors which would be hard to characterize) - so it seems it was edited well. There was not a lot of editorializing or attempts at humor which I appreciated. Apparently the authors were developers of Spark so their perspective has legitimacy. Overall I thought it was a solid book on an exciting, future oriented computing topic, and the main thing to improve upon would be to make the example code better. The naming conventions used in the code were somewhat cumbersome, but that is a topic in itself and it's always hard to name variables and functions in a way that is readable and yet not too long and confusing. Note on my reviews: I have thousands of books in my library and carefully select the next books to read in my reading list so as to have a favorable, positive experience. Therefore there is a good chance I'm going to like the book that I read next, and in turn give it a good review - I have no desire to read bad books (if someone paid me, maybe I would do it). Sometimes I am wrong and I end up reading a real clunker and you will see negative reviews from me. More than likely I will not finish the book in which case I won't review it (I only review books which I read all the way through). So yes, there is a bias in my reviews but it is not for the obvious reasons (i.e. that authors are friends of mine, or have sent me a review copy, or that I just give high ratings to everything ...)

I found this volume to be an excellent reference book for a Spark learner like me. I am a software developer, and several reviews suggested that this volume was too basic. I shouldn't have followed their advice. I bought an "advanced" book, instead, only to find myself left without material to fill in some important gaps. The information that is available on the Internet is great, but this book brings much of it together in one place. If you want to learn to think like a Spark programmer--*not* the same as thinking like a programmer--this is the place to begin.

The core Spark concepts are there but Spark: The Definitive Guide (which I subsequently purchased) would be a better purchase to make than Learning Spark. It's unfortunate there's not an updated edition of Learning Spark because it's a great introduction to Spark IMO despite the dated content in certain areas. I recommend ignoring the installation instructions altogether and just jump forward to the chapter content.

I am a software developer and wanted to learn about what Spark is. This well-written book did exactly that, starting at basic principles and moving on to more advanced topics. If I ever need to use Spark, this is the book that I will return to.

This book is beautifully written. It has everything that one could ask for: brevity, clarity, and thoroughness. These authors have the gift of making complicated ideas simple, so I would recommend this book to anyone seeking an introduction to Spark. Moreover, examples are replicated in Python, Java, and Scala so that a reader has accessible examples at his fingertips, regardless of his preference. My only suggestion would be for them to release an updated edition that reflects changes in Spark.

A good resource for people interested in learning Spark. The first 4 chapters would give you a head start and rest are for the details. In march 2015, this was the only good book available on the subject. I read its pre-released version from safari books online in late 2014 and this was the only book on Spark during that time. Quite helpful to pickup the basics. This gives you the content at one place and saves you from reading articles spread across the internet.

I bought this book for work to supplement the the data pipeline tasks that I was working on using Spark. This is a great introductory piece for important concepts such as RDD, spark job lifecycle, components of a spark job and spark job performance improvements. It provided with a good fundamental understanding of Spark that I can further enhance with researches I found online. Good read, I highly recommend for anyone who is new to Spark and is curious to learn its basics.

im a hadoop developer wanting to learn spark in java. i bought this book..its been a month now. n i feels its awesome. simply awesome. i hv one more book “Apache Spark2.0 with Java”. but first read this Learning Spark...i will teach u all the basics. amazing explanation. n given examples for all 3 languages python scala n java. though the version used for spark is 1.6 in this book, you will get a better understanding if u read this book n then move to other spark2.0 books

Well written book, fundamentals are well explained, helped me a lot with Spark initiation.

This book is amazing and it is very good introduction to Apache Spark .. instead of getting lost on the internet.

Semplice, chiaro, conciso, esaustivo, con frammenti di codice in Scala, Python e talvolta Java. In poche pagine si comprende il funzionamento di Spark e si è già in grado di impiegarlo per elaborazioni di vario tipo.

Estoy incursionando ene l mundo del analisis de datos, me impresiona como la codificacion se va simplificando en numero de lineas pero si n estas muy familiarizado con lenguajes con Python y Scala, te puede resultar un poco abstracto. Pero no es imposible de entender la sintaxis. Lo importantes es conocer Spark