I picked up MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems by Donald Miner and Adam Shook (O’Reilly) to explore the deeper analytics that were possible in using Hadoop and MapReduce. This book definitely did not disappoint in covering many of the more advanced challenges that engineers working with Hadoop datasets will encounter once they move from the simple into the advanced. MapReduce Design Patterns is not for the faint of heart nor the true novice in Hadoop and/or MapReduce frameworks. A solid understanding of the fundamentals of analytics is also a valuable prerequisite to this title.
Having explored the use of Pig and Hive as a way to abstract the underlying implementations of MapReduce, MapReduce Design Patterns helped me understand what was going on “under the hood.” This was important to me as I have learned the hard lesson that sometimes the easy way is not always the most efficient and/or effective way. By reading through this title, I now better understand how I can use Pig and Hive for the straight forward analytics and MapReduce native for my more specialized needs – or in other words – use the right tool for the job.
To the bold adventurer new to Hadoop and MapReduce – I’d suggest that you look at this book as your follow-on study guide to be used after learning the basics and working with those frameworks for a little while. In that view, I have no hesitations in recommending MapReduce Design Patterns to those engineers that are looking for something to help them move from entry level into advance levels of understanding in these technologies.
Disclaimer: I received a free electronic copy of this book as part of the O’Reilly Blogger Program