In the world of Big Data, Hadoop has become the hard-charging elephant in the room.
Its big-name users now span the alphabet and include such notables as Amazon, eBay, Facebook, Google, the New York Times, and Yahoo. Not bad for software named after a child’s toy elephant.
Computer systems that run Hadoop can store, process, and analyze large amounts of data that have been gathered up in many different formats from many different sources.
According to the Apache Software Foundation’s Hadoop website: “The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.”
The (well-trained) user defines the Big Data problem that Hadoop will tackle. Then the software handles all aspects of the job completion, including spreading out the problem in small pieces to many different computers, or nodes, in the distributed system for more efficient processing. Hadoop also handles individual node failures, and collects and combines the calculated results from each node.
But you don’t need a collection of hundreds or thousands of computers to run Hadoop. You can learn it, write programs, and do some testing and debugging on a single Linux machine, Windows PC or Mac. The Open Source software can be downloaded here. (Do some research first. You may have use web searches to find detailed installation instructions for your specific system.)
Hadoop is open-source software that is often described as “a Java-based framework for large-scale data processing.” It has a lengthy learning curve that includes getting familiar with Java, if you don’t already know it.
But if you are now ready and eager to take on Hadoop, Packt Publishing recently has unveiled three excellent how-to books that can help you begin and extend your mastery: Hadoop Beginner’s Guide, Hadoop MapReduce Cookbook, and Hadoop Real-World Solutions Cookbook.
Short reviews of each are presented below.
Hadoop Beginner’s Guide
Garry Turkington
(Packt Publishing – paperback, Kindle)
Garry Turkington’s new book is a detailed, well-structured introduction to Hadoop. It covers everything from the software’s three modes–local standalone mode, pseudo-distributed mode, and fully distributed mode–to running basic jobs, developing simple and advanced MapReduce programs, maintaining clusters of computers, and working with Hive, MySQL, and other tools.
“The developer focuses on expressing the transformation between source and result data sets, and the Hadoop framework manages all aspects of job execution, parallelization, and coordination,” the author writes.
He calls this capability “possibly the most important aspect of Hadoop. The platform takes responsibility for every aspect of executing the processing across the data. After the user defines the key criteria for the job, everything else becomes the responsibility of the system.”
The 374-page book is written well and provides numerous code samples and illustrations. But it has one drawback for some beginners who want to install and use Hadoop. Turkington offers step-by-step instructions for how to perform a Linux installation, specifically Ubuntu. However, he refers Windows and Mac users to an Apache site where there is insufficient how-to information. Web searches become necessary to find more installation details.
Hadoop MapReduce Cookbook
Srinath Perera and Thilina Gunarathne
(Packt Publishing – paperback, Kindle)
MapReduce “jobs” are an essential part of how Hadoop is able to crunch huge chunks of Big Data. The Hadoop MapReduce Cookbook offers “recipes for analyzing large and complex data sets with Hadoop MapReduce.”
MapReduce is a well-known programming model for processing large sets of data. Typically, MapReduce is used within clusters of computers that are configured to perform distributed computing.
In the “Map” portion of the process, a problem is split into many subtasks that are then assigned by a master computer to individual computers known as nodes. (Nodes also can have sub-nodes). During the “Reduce” part of the task, the master computer gathers up the processed data from the nodes, combines it and outputs a response to the problem that was posed to be solved. (MapReduce libraries are now available for many different computer languages, including Hadoop.)
“Hadoop is the most widely known and widely used implementation of the MapReduce paradigm,” the two authors note.
Their 284-page book initially shows how to run Hadoop in local mode, which “does not start any servers but does all the work within the same JVM [Java Virtual Machine]” on a standalone computer. Then, as you gain more experience with MapReduce and the Hadoop Distributed File System (HDFS), they guide you into using Hadoop in more complex, distributed-computing environments.
Echoing the Hadoop Beginner’s Guide, the authors explain how to install Hadoop on Linux machines only.
Hadoop Real-World Solutions Cookbook
Jonathan R. Owens, Jon Lentz and Brian Femiano
(Packt Publishing – paperback, Kindle)
The Hadoop Real-World Solutions Cookbook assumes you already have some experience with Hadoop. So it jumps straight into helping “developers become more comfortable with, and proficient at solving problems in, the Hadoop space.”
Its goal is to “teach readers how to build solutions using tools such as Apache Hive, Pig, MapReduce, Mahout, Giraph, HDFS, Accumulo, Redis, and Ganglia.”
The 299-page book is packed with code examples and short explanations that help solve specific types of problems. A few randomly selected problem headings:
- “Using Apache Pig to filter bot traffic from web server logs.”
- “Using the distributed cache in MapReduce.”
- “Trim Outliers from the Audioscrobbler dataset using Pig and datafu.”
- “Designing a row key to store geographic events in Accumulo.”
- “Enabling MapReduce jobs to skip bad records.”
The authors use a simple but effective strategy for presenting problems and solutions. First, the problem is clearly described. Then, under a “Getting Ready” heading, they spell out what you need to solve the problem. That is followed by a “How to do it…” heading where each step is presented and supported by code examples. Then, paragraphs beneath a “How it works…” heading sum up and explain how the problem was solved. Finally, a “There’s more…” heading highlights more explanations and links to additional details.
If you are a Hadoop beginner, consider the first two books reviewed above. If you have some Hadoop experience, you likely can find some useful tips in book number three.
— Si Dunn
