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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
Reviews of the Learning Spark: Lightning-Fast Big Data Analysis Until now we have virtually no evaluations on 'Learning Spark: Lightning-Fast Big Data Analysis' - yet it's unlikely that any of our user reviews did not keep. Download PDF (7.8 MB) Learning Spark Lightning-Fast Big Data Analysis. By Holden Karau. 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,.
This book constitutes the refereed proceedings of the Second International Conference on Data Mining and Big Data, DMBD 2017, held in Fukuoka, Japan, in July/August 2017. The 53 papers presented in this volume were carefully reviewed and selected from 96 submissions. They were organized in topical sections named: association analysis; clustering; prediction; classification; schedule and sequence analysis; big data; data analysis; data mining; text mining; deep learning; high performance computing; knowledge base and its framework; and fuzzy control.
The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science.
This book reports on the latest advances in mobile technologies for collecting, storing and processing mobile big data in connection with wireless communications. It presents novel approaches and applications in which mobile big data is being applied from an engineering standpoint and addresses future theoretical and practical challenges related to the big data field from a mobility perspective. Further, it provides an overview of new methodologies designed to take mobile big data to the Cloud, enable the processing of real-time streaming events on-the-move and enhance the integration of resource availability through the ‘Anywhere, Anything, Anytime’ paradigm. By providing both academia and industry researchers and professionals with a timely snapshot of emerging mobile bigs data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.
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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 informatio
...more Published January 28th 2015 by O'Reilly Media (first published June 1st 2014)
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Humble Book Bundle: Data Science Presented by O'Reilly 16 books — 3 voters
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Apr 07, 2017Chris Berkhout rated it it was ok
I kept thinking 'am I getting this?' only to realize that there wasn't much to get. It's just put together in an awkward way. It's not intended to be a definitive guide, and a quick starter does have a place, but it felt like a lot of space was taken by boilerplate (in code and explanation) and then there wasn't much beyond that.
The Coursera course 'Big Data Analysis with Scala and Spark' seems to cover much of the same beginner material with a more structured presentation of the ideas.
I am glad...more
The Coursera course 'Big Data Analysis with Scala and Spark' seems to cover much of the same beginner material with a more structured presentation of the ideas.
I am glad...more
Aug 10, 2014Alessandro Andrioni rated it it was amazing · review of another edition
Warning: this is a review from an early release edition of the book, containing only about five of the planned thirteen chapters
An amazing introduction to Spark, written with help from the creators of this distributed computing framework. Definitely much better than just reading the documentation, especially if you are interested on PySpark, the Python API to Spark, which is currently under-documented and pretty much hit-and-miss.
An amazing introduction to Spark, written with help from the creators of this distributed computing framework. Definitely much better than just reading the documentation, especially if you are interested on PySpark, the Python API to Spark, which is currently under-documented and pretty much hit-and-miss.
Jul 02, 2018Gavin rated it it was ok
Tool books are difficult to stomach: their contents are so much more ephemeral than other technical books. It's not worth it: in 10 years, will it matter? etc. (This is an incredibly high bar to pose, but that's how high my opinion is of the technical pursuits.) O'Reilly soften this blow, occasionally, by enlisting really brilliant authors who bring in the eternal and the broad while pootering around their narrow furrow. (I am incredibly fond of Alan Gates for this, for instance.)
Spark is the b...more
Spark is the b...more
I think it's really good book to get started with Spark. Since I already use Spark at work, I went quite fast as I already knew a lot of what is in the book. Still learned many new things.
It has code samples in Java, Scala and Python. It covers Spark 1.x, while Spark 2.x is already out with quite bit of changes. So you might have to adjust these samples.
It has code samples in Java, Scala and Python. It covers Spark 1.x, while Spark 2.x is already out with quite bit of changes. So you might have to adjust these samples.
The book is good as a starter kit but doesn't go too much in spark internals
The book is good as a starter kit but doesn't go too much in spark internals. The book is setup in a good manner and has a very smooth flow and can be read in one or two sitting
The book is good as a starter kit but doesn't go too much in spark internals. The book is setup in a good manner and has a very smooth flow and can be read in one or two sitting
Learning Spark Lightning-fast Big Data Analysis Pdf Download Windows 7
Great book on Spark.
A nice general overview of popular Spark components with examples in Scala, Java and Python. Python code does not respect PEP-8.
The book gives only a shallow knowledge of Spark
Great introduction to Spark.
I've learned much more from online Moocs than this book.
The book is good for beginners of Spark. The author is one of the team members who wrote spark system. So, the intuitive thinking and concepts are definitely correct. This is very important for the beginners.
Jan 18, 2017The Viet Nguyen rated it really liked it
This is my second review for Learning Spark. The first time I read this book it does not make much sense to me because I don’t have much experience with either Spark or Scala.
After a while I have an interesting project that requires some Spark knowledge. And this book was my choice again because most of my expert friends still recommend it. The picture turned out completely different this time.
With some hand on experience with Scala, Spark could be viewed just as another immutable collection fr...more
After a while I have an interesting project that requires some Spark knowledge. And this book was my choice again because most of my expert friends still recommend it. The picture turned out completely different this time.
With some hand on experience with Scala, Spark could be viewed just as another immutable collection fr...more
Jul 20, 2016Nitish Sheoran rated it really liked it
Learning Spark Lightning-fast Big Data Analysis Pdf Download Free
When I started this book, I was basically looking for a book which can give me a good introduction to Apache spark and pyspark. This book was quite a good learning experience with programming examples with more emphasis on Java/Scala than Python. Pyspark is still less developed than Scala/Java, but there are still places where more detailed python examples can be included. So in conclusion, one of the best book for introducing Apache Spark and learning Spark using Java/Scalain market but lagging...more
Quite good introduction to apache spark for both engineers and data scientists. The book focuses on practical approach and I think that even experienced people can learn a little bit from it.
One thing to note is that the book covers big data environment or data science in a general view. So it cannot be treated as a comprehensive source of knowledge, and should rather be considered like another book for the shelf of before mentioned topics.
One thing to note is that the book covers big data environment or data science in a general view. So it cannot be treated as a comprehensive source of knowledge, and should rather be considered like another book for the shelf of before mentioned topics.
Feb 08, 2015Krishna Sangeeth rated it really liked it · review of another edition
The perks of Safari membership. I read on and off and covered most of it. It was super useful initially to understand concepts.
The API docs of Spark are pretty good and they would be suffice most of the time. But this being one of the first books written about Spark, it certainly adds a more holistic view on Spark.
I would recommend videos from Spark Summit especially the workshop ones as a good add on to the book
The API docs of Spark are pretty good and they would be suffice most of the time. But this being one of the first books written about Spark, it certainly adds a more holistic view on Spark.
I would recommend videos from Spark Summit especially the workshop ones as a good add on to the book
Jul 20, 2016Vilhelm Ehrenheim rated it really liked it
Well written, to the point introduction to Spark. It gives examples in all three api languages of python, scala and java where applicable. This is really nice but also bit annoying at times. I also read an edition of the book for an older version of Spark which was a bit irritating when trying things out but that was really my own fault. I think there are newer editions.
In general I recommend this book if you want to start playing w Spark.
In general I recommend this book if you want to start playing w Spark.
Oct 05, 2016Daniel rated it really liked it
A lot of content is outdated: the book references Spark 1.3 but the current version is 2.0, many of the code examples won't work. However the idea behind the code is still valid, a visit to the documentation should be enough to update the code.
Interesting read for those that want an introduction to Apache Spark.
It could benefit from an introduction chapter on graphing, similar like the one already in on machine learning. What's in is good.
It could benefit from an introduction chapter on graphing, similar like the one already in on machine learning. What's in is good.
The authors do a a good job at explain how Spark works, and how to use it. Worth reading!
Mar 23, 2015Sathya Narayanan rated it really liked it
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Apr 20, 2015
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Sep 12, 2017
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Apr 07, 2015
Apr 07, 2015
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“Note that the hash function you pass will be compared by identity to that of other RDDs. If you want to partition multiple RDDs with the same partitioner, pass the same function object (e.g., a global function) instead of creating a new lambda for each one!” — 0 likes
Learning Spark Lightning-fast Big Data Analysis Pdf Download Windows 7
“For each input source, Spark Streaming launches receivers, which are tasks running within the application’s executors that collect data from the input source and save it as RDDs. These receive the input data and replicate it (by default) to another executor for fault tolerance. This data is stored in the memory of the executors in the same way as cached RDDs.1” — 0 likes