PDF Ebook Advanced Analytics with Spark: Patterns for Learning from Data at Scale
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Advanced Analytics with Spark: Patterns for Learning from Data at Scale
PDF Ebook Advanced Analytics with Spark: Patterns for Learning from Data at Scale
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About the Author
Sandy Ryza develops algorithms for public transit at Remix. Prior, he was a senior data scientist at Cloudera and Clover Health. He is an Apache Spark committer, Apache Hadoop PMC member, and founder of the Time Series for Spark project. He holds the Brown University computer science department's 2012 Twining award for "Most Chill".Uri Laserson is an Assistant Professor of Genetics at the Icahn School of Medicine at Mount Sinai, where he develops scalable technology for genomics and immunology using the Hadoop ecosystem.Sean Owen is Director of Data Science at Cloudera. He is an ApacheSpark committer and PMC member, and was an Apache Mahout committer.Josh Wills is the Head of Data Engineering at Slack, the founder of the Apache Crunch project, and wrote a tweet about data scientists once.
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Product details
Paperback: 280 pages
Publisher: O'Reilly Media; 2 edition (July 6, 2017)
Language: English
ISBN-10: 9781491972953
ISBN-13: 978-1491972953
ASIN: 1491972955
Product Dimensions:
7 x 0.6 x 9.2 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review:
4.3 out of 5 stars
33 customer reviews
Amazon Best Sellers Rank:
#68,795 in Books (See Top 100 in Books)
This book fills an important gap in large scale data science.Spark has emerged as the big data platform of choice for data scientists both from the ease of use as well as the performance / optimization point of view. In a few lines of Scala code, Spark allows you to write iterative algorithms that scale out very well. For a data scientist who wants to explore large scale data sets, Spark is a great starting point (this is incredible progress in the Spark community given the project is just about 4 years old). However, Spark itself is moving fast and maturing with time, and Spark and Scala as well as distributed algorithms are typically not in the arsenal of many data scientists today.What this book does is teach you how to think about data science problems at scale, in the context of Spark. By well chosen examples covering both supervised and unsupervised learning, the authors take you step by step from a practical problem definition (say how to recommend music given user's history of music listened to) to what features are relevant, what machine learning algorithm to use and how to tune parameters to optimize the solution and how you can use Spark to do all of this in an interactive / iterative manner. As a bonus, they also point you to well engineered data sets that you can use to follow along the discussion and learn by trying out the examples yourself.By embracing the feature engineering steps and data cleaning/ error handling and tuning /feedback steps, the authors manage to show how real world data science works and how you can do full stack data science using Spark and gain immensely from the interactive nature of the Spark REPL.Overall, I highly recommend this book, and though it is the first book on Data Science using Spark, it sets a high standard for subsequent efforts.
It is a so, so book. Examples are okay and the codes provided are "elegant" - certainly the result of spending hours and hours optimizing them; but that is not what a typical Spark users will face in life. The explanations are hurried and they make it very hard for the reader to connect the dots. It seems that the book's intent was right, but the application was woefully inadequate. If you do all the work in the book, you will be very competent at reading csv files - but is about all. The authors have a habit of providing esoteric "helper" functions to clean up the files but you don't really understand what is happening because either the explanations are thin or there is none to be found. A big part of data science is preparing the data - anyone can turn the crank on clean data but how do you go from the start to finish. This was their opportunity and they left a big gap. Spark's ML examples are nicer than what is presented in this book; paying for a book to get minimal information is a bit odd. I was really looking forward to going through this book and I am glad I did; it makes me appreciate authors who spend time writing good books.
TL;DR If you are looking for a intro to data science, data analysis and machine learning at scale - this is the right book. Sure, there are others, maybe more popular books from O'Reilly considering these topics, but the authors of those are using R and Python and the books are not focused on the performance and scalability. For closer details regarding Spark you can also take a look at this introductory Spark book - Learning Spark.This book presents 9 case studies of data analysis applications in various domains. The topics are diverse and the authors always use real world datasets. Beside learning Spark and a data science you will also have the opportunity to gain insight about topics like taxi traffic in NYC, deforestation or neuroscience. Without any previous exposure or contact with machine learning readers might struggle to understand certain chapters, so I think it's good idea to actually try those examples yourself while reading and Google for further details about the used methods. Many of the chapters end only with basic models, which barely outperform the baselines, so if you want to, there is a lot of space for their improvement and further work.Spark itself provides it's users with APIs in three languages - Java, Scala and Python. This books successfully covers each one of these, although you can feel slight preference of a Scala throughout the book. For Scala starters - they always explain some of the special constructs or syntax features which is in fact a nice thing. Introduction and Appendix chapters provides basic information about the Spark core, RDDs (Resilient distributed datasets) or options of running Spark - whether in cluster (Mesos, YARN, Spark's own) or standalone settings. Throughout the book you can find some really worthy tips about Spark or data analysis - like using other serializer than the Java's default (they recommend kryo), overview of data cleansing and whole machine learning pipeline. To sum up, I recommend this book to every data scientist - because it demonstrates advanced topics like workload distribution and scaling on an enjoyable examples.
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