Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large Scale Data Analysis 1st ed. Edition

Mr. Guller has done a fabulous job by writing "Big Data Analytics with Spark". It's a very good introductory book for anyone to get started. It walks through numerous set of examples, including a primer to Bigdata and Scala. Then it moves on to Spark. A must have for all the beginners.

I liked the book. it nice and simple and good for beginners.One issue I found is that this book covers the the entire spark eco-system (spark core, spark sql, spark streaming, mllib) in a very brief way. it doesn't go deep into any of the topics. This makes it very similar to the spark programming guide on the web which also adopts a similar pattern.What would have been nice if the author would have gone deep into spark-core. Upon reading this book, it didn't teach me anything more than the spark programming guide

Hi,I have written a detailed chapter-by-chapter review of this book on www DOT i-programmer DOT info, the first and last parts of this review are given here. For my review of all chapters, search i-programmer DOT info for STIRK together with the book's title.This book aims to provide a “...concise and easy-to-understand tutorial for big data and Spark”. How does it fare?Spark is increasing the tool of choice for big data processing, being much faster than Hadoop’s MapReduce. After putting Spark into a big data context, the book aims to cover Spark’s core library, together with its more specialized libraries for Streaming, Machine Learning, SQL, and Graphing.The book is aimed at developers that are new to Spark, some general background programming knowledge required, but little else.Chapter 1 Big Data Technology LandscapeThis chapter opens with a discussion about the current big data age, with data as the lifeblood of organizations, and growing exponentially. The standard 3Vs definition of big data is explored (velocity, variety, volume). Traditional relational database management systems (RDBMS) are unable to process these large volumes in a timely manner – this is where the scalability of big data systems comes into its own.Next, the chapter discusses some technologies that are either used with Spark, or Spark competes with. The first technology is Hadoop, this is fault tolerant and scalable, and runs on commodity hardware. The three major components of Hadoop are discussed: YARN (Yet Another Resource Negotiator), MapReduce (distributed processing model), and HDFS (Hadoop Distributed File System). Spark is increasingly being used in place of MapReduce owning to its faster speed. The section briefly discusses Hive, a data warehouse with a SQL like interface, Spark SQL is expected to supersede Hive on many systems.The chapter continues with a look at some common binary formats for serializing (storing on disk) big data, and their pros and cons. Specifically Avro, Thrift, Protocol Buffers, and SequenceFile are examined. Next, some column storage formats, which have performance advantages when the client requires a subset of columns, were briefly discussed, namely: RCFile, ORC, and Parquet.Then a brief overview of messaging systems is provided, together with the advantages of having a layer of abstraction between producers and consumers. Specifically, Kafka and ZeroMQ are discussed with the aid of useful supporting diagrams.NoSQL is then examined. The various types of NoSQL databases have different aims to the traditional RDBMS, typically trading Atomicity, Consistency, Isolation, Durability (ACID) for scalability and flexibility. The specific NoSQL databases briefly discussed are Cassandra and HBase. I sometimes wonder if it is meaningful to group NoSQL databases together. Is it meaningful to divide sports into Football and NoFootball? Are all the NoFootball sports meaningful as a group?The chapter ends with a look at some distributed SQL query engines, these do not use MapReduce batch jobs, and are thus more oriented to interactive querying. The engines briefly examined are: Impala, Presto, and Apache Drill.This chapter provides an excellent overview of big data technology. It should be noted there are many more technologies than described, but the examples given are sufficient to explain the topic areas. This is possibly the best backgrounder to big data I’ve read.The discussions are very well written, concise and clear, with helpful diagrams, and no wasted words. There’s a good flow between the topics, and useful links between chapters. There are website links for further information. These traits apply to all the chapters in the book....ConclusionThis book aims to provide a “...concise and easy-to-understand tutorial for big data and Spark”, and clearly succeeds. The book is exceptionally well written. Helpful explanations, diagrams, practical step-by-step walkthroughs, annotated code, inter-chapter links, and website links abound throughout.The book is aimed at developers that are new to Spark, and explains concepts from the beginning. If you work through the book you should become competent in the use of Spark, there is much more to learn of course, but this book gives a solid foundation in both core Spark and its major specialized libraries: Streaming, Machine Learning, SQL, and Graphing.The book is based on workshops given by the author, and clearly the feedback from these has been useful in creating this book, since it seems to have answered all the questions I had.This book provides everything you need to know to get started with Spark, explained in an easy-to-follow manner. If you want to learn Spark, buy this book. Highly recommended

After picking up the basics of Scala (from books like Scala for the Impatient, the Scala CookBook and blogs), I tried reading up on Spark. This book was very useful for me because its in Scala, and Scala only. Unlike some other books that show samples in Java/Python/Scala, having only Scala reduces clutter and bulk. But yes, unfortunately, it is not in Python.The writing is pretty good, the editing is also good (I am mentioning this because a bunch of Scala/Spark books out there have terrible, sometimes incomprehensible, language)The examples are fairly complete. The sections on SQL, Spark-Streaming are less verbose and more example-filled. After each example, there is description of the code.Of all the books so far, this had the most pleasant introduction to Machine Learning. Its purely from a software development perspective - with sample use cases and show casing an appropriate MLLib library. The Spark ML section is a bit rushed, but has enough samples to get started via blogs, and the Apache site.