--- product_id: 12637740 title: "Learning Spark: Lightning-Fast Big Data Analysis" price: "€ 26.29" currency: EUR in_stock: true reviews_count: 13 url: https://www.desertcart.pt/products/12637740-learning-spark-lightning-fast-big-data-analysis store_origin: PT region: Portugal --- # Learning Spark: Lightning-Fast Big Data Analysis **Price:** € 26.29 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Learning Spark: Lightning-Fast Big Data Analysis - **How much does it cost?** € 26.29 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.pt](https://www.desertcart.pt/products/12637740-learning-spark-lightning-fast-big-data-analysis) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3 , this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables Review: Great start to learn Apache Spark - 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! Review: No-nonsense attempt at explaining Spark - 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 ...) ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #2,035,922 in Books ( See Top 100 in Books ) #826 in Data Processing #1,087 in Enterprise Applications #2,366 in Software Development (Books) | | Customer Reviews | 4.3 out of 5 stars 345 Reviews | ## Images ![Learning Spark: Lightning-Fast Big Data Analysis - Image 1](https://m.media-amazon.com/images/I/91DSHOyVTDL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Great start to learn Apache Spark *by L***C on February 24, 2015* 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! ### ⭐⭐⭐⭐ No-nonsense attempt at explaining Spark *by K***E on January 3, 2016* 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 ...) ### ⭐⭐⭐⭐⭐ Start here: Excellent reference for Spark *by B***I on June 16, 2015* 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. ## Frequently Bought Together - Learning Spark: Lightning-Fast Big Data Analysis - Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems - Spark: The Definitive Guide: Big Data Processing Made Simple --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.pt/products/12637740-learning-spark-lightning-fast-big-data-analysis](https://www.desertcart.pt/products/12637740-learning-spark-lightning-fast-big-data-analysis) --- *Product available on Desertcart Portugal* *Store origin: PT* *Last updated: 2026-06-11*