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Fast Data Processing with Spark
Spark is a framework 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 inbuilt tools for interactive query analysis (Shark), large-scale graph processing and analysis (Bagel), and real-time analysis (Spark Streaming), it can be interactively used to quickly process and query big data sets.
Fast Data Processing with Spark covers how to write distributed map reduce style 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 deploying your job to the cluster, and tuning it for your purposes.
Fast Data Processing with Spark covers everything from setting up your Spark cluster in a variety of situations (stand-alone, EC2, and so on), to how to use the interactive shell to write distributed code interactively. From there, we move on to cover how to write and deploy distributed jobs in Java, Scala, and Python.
We then examine how to use the interactive shell to quickly prototype distributed programs and explore the Spark API. We also look at how to use Hive with Spark to use a SQL-like query syntax with Shark, as well as manipulating resilient distributed datasets (RDDs).
Fast Data Processing with Spark covers how to write distributed map reduce style 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 deploying your job to the cluster, and tuning it for your purposes.
Fast Data Processing with Spark covers everything from setting up your Spark cluster in a variety of situations (stand-alone, EC2, and so on), to how to use the interactive shell to write distributed code interactively. From there, we move on to cover how to write and deploy distributed jobs in Java, Scala, and Python.
We then examine how to use the interactive shell to quickly prototype distributed programs and explore the Spark API. We also look at how to use Hive with Spark to use a SQL-like query syntax with Shark, as well as manipulating resilient distributed datasets (RDDs).
- GenresComputer Science
120 pages, ebook
First published January 1, 2013
5 people are currently reading
26 people want to read
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Displaying 1 - 5 of 5 reviews
December 21, 2020
No entiendo porque es un libro que tiene notas de dos estrellas. Es conciso y toca muchos elementos fundacionales de Spark, qué ayudan a entender cómo es que funciona.
Creo que muchas personas se esperan on Coookbook o terminar entendiendo cuándo usar qué o cuál función y por eso se desiluciónan cuándo lo leen.
El libro tiene dos inconvenientes. El primero es que al ser del 2013 hace incapié en Hive y no le da tanta importancia a la API de Python. Esto hoy en día no es tan útil debido a que el uso de Pyspark es muchísimo mayor que en aquella época y tal vez sea el más usado en los próximos años. El segundo inconveniente lo veo en las partes donde lista las funciones en una suerte de vademecum. Sólo sirve si uno tiene algún recorrido con Spark y buscar un conocimiento un poco mayor.
El libro es bueno . Hasta tiene un capítulo de testing. Adicionalmente, está bien escrito. Algo muy apreciable en el mundo tecnológico. Sólo le pongo 4 estrellas porque está un poco desactualizado.
Creo que muchas personas se esperan on Coookbook o terminar entendiendo cuándo usar qué o cuál función y por eso se desiluciónan cuándo lo leen.
El libro tiene dos inconvenientes. El primero es que al ser del 2013 hace incapié en Hive y no le da tanta importancia a la API de Python. Esto hoy en día no es tan útil debido a que el uso de Pyspark es muchísimo mayor que en aquella época y tal vez sea el más usado en los próximos años. El segundo inconveniente lo veo en las partes donde lista las funciones en una suerte de vademecum. Sólo sirve si uno tiene algún recorrido con Spark y buscar un conocimiento un poco mayor.
El libro es bueno . Hasta tiene un capítulo de testing. Adicionalmente, está bien escrito. Algo muy apreciable en el mundo tecnológico. Sólo le pongo 4 estrellas porque está un poco desactualizado.
May 3, 2014
First (and only - so far) book about Spark.
Sadly, it's bad. Yes, not average, just bad.
The shortest description is - a list of short code exercises with particular Spark usage examples:
* no proper introduction (why Spark? how does Spark differ? what's its advantage?)
* barely any architecture intro
* no overview (that could get readers familiar with idea what Spark is capable with, in general)
Code examples miss a lot of context, some of them are just the same example, but for instance: in 3 languages: Java, Scala & Python. Or: 25% of the book is about various Spark installation walkthrough (all of them are still the most basic option...).
Actual API description is poor - just 1 big table for each of the languages. Topics like broadcasting or accumulation are simplified to the limits...
If you start this book to learn Spark and you didn't know it earlier -> it's a waste of time and money.
If you know Spark basics and you just want help with getting through writing few first programs ('Hello World'-level cases) -> it may be something you find helpful.
Why 2 stars and not 1 star then? First, because it's still the only book about Spark (and Spark misses good documentation anyway) and because if you acknowledge limited book scope, what's there is quite well structured and organized.
Sadly, it's bad. Yes, not average, just bad.
The shortest description is - a list of short code exercises with particular Spark usage examples:
* no proper introduction (why Spark? how does Spark differ? what's its advantage?)
* barely any architecture intro
* no overview (that could get readers familiar with idea what Spark is capable with, in general)
Code examples miss a lot of context, some of them are just the same example, but for instance: in 3 languages: Java, Scala & Python. Or: 25% of the book is about various Spark installation walkthrough (all of them are still the most basic option...).
Actual API description is poor - just 1 big table for each of the languages. Topics like broadcasting or accumulation are simplified to the limits...
If you start this book to learn Spark and you didn't know it earlier -> it's a waste of time and money.
If you know Spark basics and you just want help with getting through writing few first programs ('Hello World'-level cases) -> it may be something you find helpful.
Why 2 stars and not 1 star then? First, because it's still the only book about Spark (and Spark misses good documentation anyway) and because if you acknowledge limited book scope, what's there is quite well structured and organized.
February 11, 2016
PACKT Publishing tends to be first-to-market with books on big data technologies If you're brand new to Spark and want to drink from a short 100 page fire hose, then go for it. Experienced Spark users will probably not benefit.
April 26, 2015
The book covers about 20 minutes of real knowledge, disappointing to see no real book about Spark that is challenging yet.
December 17, 2015
It is very brief book that aimed to cover a lot of material in only 120 pages. Mostly a waste of time.
Displaying 1 - 5 of 5 reviews