Cloudera Administration Handbook – How to become an effective Big Data administrator of large Hadoop clusters – #bookreview



Cloudera Administration Handbook

 Rohit Menon

Packt PublishingKindle, paperback


The explosive growth and use of Big Data in business, government, science and other arenas has fueled a strong demand for new Hadoop administrators. The administrators’ key duty is to set up and maintain Hadoop clusters that help process and analyze massive amounts of information.

New Hadoop administrators and those looking to join their ranks especially will want to give good consideration to The Cloudera Administration Handbook by Rohit Menon. This is a well-organized, well-written and solidly illustrated guide to building and maintaining large Apache Hadoop clusters using Cloudera Manager and CDH5.

The author has an extensive computer science background and is a Cloudera Certified Apache Hadoop Developer. He notes that “Cloudera Inc., is a Palo Alto-based American enterprise software company that provides Apache Hadoop-based software, support and services, and training to data-driven enterprises. It is often referred to as the commercial Hadoop company.”

CDH, Menon points out, is the easy shorthand name for a rather awkward software title: “Cloudera’s Distribution Including Apache Hadoop.” CDH is “an enterprise-level distribution including Apache Hadoop and several components of its ecosystem such as Apache Hive, Apache Avro, HBase, and many more. CDH is 100 percent open source,” Menon writes.

The Cloudera Manager, meanwhile, “is a web-browser-based administration tool to manage Apache Hadoop clusters. It is the centralized command center to operate the entire cluster from a single interface. Using Cloudera Manager, the administrator gets visibility for each and every component in the cluster.”

The Cloudera Manager is not explored until nearly halfway into the book, and some may wish it had been explained sooner, since they may be trying to learn it on day one of their new job. However, Menon wants readers first to become familiar with “all the steps and operations needed to set up a cluster via the command line” at a terminal. And these are, of course, important considerations to becoming an effective, knowledgeable and versatile Hadoop Administrator.  (You may not always have access to Cloudera Manager while setting up or troubleshooting a cluster.)

The book’s nine chapters show its well-focused range:

  • Chapter 1: Getting Started with Apache Hadoop
  • Chapter 2: HDFS and MapReduce
  • Chapter 3: Cloudera’s Distribution Including Apache Hadoop
  • Chapter 4: Exploring HDFS Federation and Its High Availability
  • Chapter 5: Using Cloudera Manager
  • Chapter 6: Implementing Security Using Kerberos
  • Chapter 7: Managing an Apache Hadoop Cluster
  • Chapter 8: Cluster Monitoring Using Events and Alerts
  • Chapter 9: Configuring Backups

You will have to bring some hardware and software experience and skills to the table, of course. Apache Hadoop primarily is run on Linux. “So having good Linux skills such as monitoring, troubleshooting, configuration, and security is a must” for a Hadoop administrator, Menon points out. Another requirement is being able to work comfortably with the Java Virtual Machine (JVM) and understand Java exceptions.

But those skills and his Cloudera Administration Handbook can take you from “the very basics of Hadoop” to taking up “the responsibilities of a Hadoop administrator and…managing huge Hadoop clusters.”

Si Dunn

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Programming MapReduce with Scalding – Using Hadoop & Scala to do some Big Data – #programming #bookreview

Programming MapReduce with Scalding

Programming MapReduce with Scalding

A practical guide to designing, testing, and implementing complex MapReduce applications in Scala

Antonios Chalkiopoulos

(Packt Publishing – paperback, Kindle)


Antonio Chalkiopoulos’s new book has three key goals, and it meets each of them in good, readable fashion.

It describes how MapReduce, Hadoop, and Scalding can work together. It suggests some useful design patterns and idioms. And, it provides numerous code examples of “real implementations for common use cases.”

The book also briefly introduces the Scala programming language and the Cascading platform, two elements vital to the Scalding framework.

Right here, a few brief definitions need to be offered.

According to a Wikipedia definition, MapReduce is both a programming model and “an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster.”

Meanwhile, the Apache Hadoop website states: “Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.”

And: “The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware” and “is highly fault-tolerant….”

Continuing, the website promotes Cascading as “the proven application development platform for building data applications on Hadoop.” Plus, Scalding, it explains, is  “an extension to Cascading that enables application development with Scala, a powerful language for solving functional problems.” Indeed, Scalding is “[a] Scala API for Cascading,” and it provides functionality from custom join algorithms to multiple APIs (Fields-based, Type-safe, Matrix) for developers to build robust data applications. Scalding is built and maintained by Twitter.”

Scalding “makes MapReduce computations look very similar to Scala’s collection API. It’s also a wrapper for Cascading to simplify jobs, tests and data sources on HDFS or local disk.”

Okay, that’s a lot to digest, especially if you are making some of your first forays into the world of Big Data.

Fortunately, Programming MapReduce with Scalding offers clear, well-illustrated, smoothly paced how-to steps, as well as easy-to-digest definitions and descriptions. It takes the reader from setting up and running a Hadoop mini-cluster and local-development environment to applying  Scalding to real-use cases, as well as developing good test and test-driven development methodologies, running Scalding in production, using external data stores, and applying matrix calculations and machine learning.

The book is written for developers who have “a basic understanding” of Hadoop and MapReduce, but is also intended for experienced Hadoop developers who may be “enlightened by this alternative methodology of developing MapReduce applications with Scalding.”

In this book, “[a] Linux operating system is the preferred environment for Hadoop.” And the author includes instructions for how to install and use a Kiji Bento Box, “a zero-configuration bundle that provides a suitable environment for testing and prototyping projects that use HDFS, MapReduce, and HBase with minimal setup time.”  It’s an easy way to get Apache Hadoop up and running in as little as five minutes or so.

Or, if you prefer, you can manually install the required software packages. Either way, you can learn a lot and do a lot with a Hadoop mini-cluster. And, with this book, you can get a very good handle on the Scalding API.

It does help to be somewhat familiar with MapReduce, Scalding, Scala, Hadoop, Maven, Eclipse and the Linux environment.  But Antonio Chalkiopoulo does a good job of keeping the examples accessible even when readers are new to some of the packages. Still, be prepared to take your time and be prepared to do some additional research on the web and ask questions in forums, particularly if any of the required software is new to you.

(The book also can be purchased direct from Packt Publishing at


Si Dunn





Hadoop is hot! Three new how-to books for riding the Big Data elephant – #programming #bookreview

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

MapReduce Design Patterns – For solving Big Data problems – #bookreview #programming #hadoop

MapReduce Design Patterns
Donald Miner and Adam Shook
(O’Reilly –
paperback, Kindle)

“MapReduce is a computing paradigm for processing data that resides on hundreds of computers,” the authors point out. It has been “popularized recently by Google, Hadoop, and many others.”

The MapReduce paradigm is “extraordinarily powerful, but does not provide a general solution to what many are calling ‘big data,” they add, “so while it works particularly well on some problems, some are more challenging.” The authors’ focus in their new book is on using MapReduce design patterns as “templates or general guides to solving problems.”

Their new book definitely can help solve some time-crunch problems for new MapReduce developers. It brings together a variety of solutions that have emerged over time in a patchwork of blogs, books, and research papers and explains them in detail, with code samples, illustrations, and cautions about potential pitfalls.

You can’t simply cut and paste solutions from the chapters. But the two writers do “hope that you will find a pattern to get you at least 90% of the way for just about all of your challenges.”

Six of the book’s eight chapters focus on specific types of design patterns:

  • Summarization Patterns
  • Filtering Patterns
  • Data Organization Patterns
  • Join Patterns
  • Metapatterns
  • Input and Output Patterns

“The MapReduce world is in a state similar to the object-oriented world before 1994,” the authors point out. “Patterns today are scattered across blogs, websites such as StackOverflow, deep inside other books, and inside very advanced technology teams at organizations across the world.”

They add that “[t]he intent of this book is not to provide some groundbreaking new ways to solve problems with MapReduce….” but to offer, instead, a collection of “patterns that have been developed by veterans in the field so they can be shared with everyone else.”

The book’s code samples are all written in Hadoop, and the two writers deal with the question of “why should we use Java MapReduce in Hadoop at all when we have options like Pig and Hive,” which reduce the need for MapReduce patterns.

There is “conceptual value,” they state, “in understanding the lower level workings of a system like MapReduce.” Furthermore, “Using Pig or Hive without understanding MapReduce can lead to some dangerous situations.” And, Pig and Hive cannot yet “tackle all of the problems in the ways that Java MapReduce can. This will surely change over time….”

If you are new to MapReduce, this useful and informative book from Donald Miner and Adam Shook can be the next best thing to having MapReduce experts at your side.

MapReduce Design Patterns can save you time and effort, steer you away from dead ends, and help give you solid understandings of the powerful MapReduce paradigm.

Si Dunn

Spring Data: Modern Data Access for Enterprise Java – #java #bookreview

Spring Data: Modern Data Access for Enterprise Java
Mark Pollack, Oliver Gierke, Thomas Risberg, Jonathan L. Brisbin and Michael Hunger
(O’Reilly, paperbackKindle)

Big Data keeps getting wider and deeper by the second. And so do the demands for analyzing and profiting from all of those piled-up terabytes.

Meanwhile, the once whiz-bang technology known as the relational database is having a very hard time keeping pace. The sheer amount of data that companies now gather, store, access, and analyze is pushing traditional relational databases to the breaking point.

Many Java developers who are trying to keep these overloaded systems held together with baling wire, also are starting to learn to work with some of the “alternative data stores that are being used in mission-critical enterprise applications,” the authors of Spring Data point out.

A lot of data now is being stored elsewhere and not in relational databases. Yet companies cannot abandon what they have already gathered and invested heavily to access. So they need to keep using and supporting their relational databases, plus some newer, faster, more voracious solutions lumped under the heading “NoSQL databases,” (even though you can query them).

In “the new data access landscape,” the authors note: “there is a revolution taking place, which for data geeks is quite exciting. Relational databases are not dead; they are still central to the operations of many enterprises and will remain so for quite some time. The trends, though, are very clear: new data access technologies are solving problems that traditional relational databases can’t, so we need to broaden our skill set as developers and have a foot in both camps.”

They add: “The Spring Framework has a long history of simplifying the development of Java applications, in particular for writing RDBMS-based data access layers that use Java database connectivity (JDBC) or object-relational mappers.”

Their new book “is intended to give you a hands-on introduction to the Spring Data project, whose core mission is to enable Java developers to use state-of-the-art data processing and manipulation tools but also use traditional databases in a state-of-the-art manner.”

They have organized their 288-page book into six parts and 14 chapters:

Part I – Background

  • Chapter 1 – The Spring Data Project
  • Chapter 2 – Repositories: Convenient Data Access Layers
  • Chapter 3 – Type-Safe Querying Using Querydsl

Part II – Relational Databases

  • Chapter 4 – JPA Repositories
  • Chapter 5 – Type-safe JDBC Programming with Querydsl SQL

Part III – NoSQL

  • Chapter 6 – MongoDB: A Document Store
  • Chapter 7 – Neo4j: A Graph Database
  • Chapter8 – Redis: A Key/Value Store

Part IV – Rapid Application Development

  • Chapter 9 – Persistence Layers with Spring Roo
  • Chapter 10 – REST Repository Exporter

Part V – Big Data

  • Chapter 11 – Spring for Apache Hadoop
  • Chapter 12 – Analyzing Data with Hadoop
  • Chapter 13 – Creating Big Data Pipelines with Spring Batch and Spring Integration

Part 5 – Data Grids

  • Chapter 14 – GemFire: A Distributed Data Grid

“Many of the values that have made Spring the preferred platform for enterprise Java developers deliver particular benefit in a world of fragmented persistence solutions,” states Ron Johnson, creator of Spring Framework. Writing in the book’s foreword, he notes: “Part of the value of Spring is how it brings consistency (without descending to a lowest common denominator) in its approach to different technologies with which it integrates.

“A distinct ‘Spring way’ helps shorten the learning curve for developers and simplifies code maintenance. If you are already familiar with Spring, you will find that Spring Data eases your exploration and adoption of unfamiliar stores. If you aren’t already familiar with Spring, this is a good opportunity to see how Spring can simplify your code and make it more consistent.”

Spring Data definitely is not light reading, but it is well-written, and provides a good blending of procedures, steps, explanations, code samples, screenshots and other illustrations.

Si Dunn

Big Data Book Blast: Hadoop, Hive…and Python??? – #programming #bookreview

Big Data is hothotHOT. And O’Reilly recently has added three new books of potential interest to Big Data workers, as well as those hoping to join their ranks.

Hadoop, Hive and–surprise!—Python are just a few of the hot tools you may encounter in the rapidly expanding sea of data now being gathered, explored, stored, and manipulated by companies, organizations, institutions, governments, and individuals around the planet. Here are the books:

Hadoop Operations
Eric Sammer
(O’Reilly, paperbackKindle)

“Companies are storing more data from more sources in more formats than ever before,” writes Eric Sammer, a Hadoop expert who is principal solution architect at Cloudera. But gathering and stockpiling data is only “one half of the equation,” he adds. “Processing that data to produce information is fundamental to the daily operations of every modern business.”

Enter Apache Hadoop, a “pragmatic, cost-effective, scalable infrastructure” that increasingly is being used to develop Big Data applications for storing and processing information.

“Made up of a distributed filesystem called the Hadoop Distributed Filesystem (HDFS) and a computation layer that implements a processing paradigm called MapReduce, Hadoop is an open source, batch data processing system for enormous amounts of data. We live in a flawed world, and Hadoop is designed to survive in it by not only tolerating hardware and software failures, but also treating them as first-class conditions that happen regularly.”

Sammer adds: “Hadoop uses a cluster of plain old commodity servers with no specialized hardware or network infrastructure to form a single, logical, storage and compute platform, or cluster, that can be shared by multiple individuals or groups. Computation in Hadoop MapReduce is performed in parallel, automatically, with a simple abstraction for developers that obviates complex synchronization and network programming. Unlike many other distributed data processing systems, Hadoop runs the user-provided processing logic on the machine where the data lives rather than dragging the data across the network; a huge win for performance.”

Sammer’s new, 282-page book is well written and focuses on running Hadoop in production, including planning its use, installing it, configuring the system and providing ongoing maintenance. He also shows “what works, as demonstrated in crucial deployments.”

If you’re new to Hadoop or still getting a handle on it, you need Hadoop Operations. And even if you’re now an “old” hand at Hadoop, you likely can learn new things from this book. “It’s an extremely exciting time to get into Apache Hadoop,” Sammer states.

Programming Hive
Eric Capriolo, Dean Wampler, and Jason Rutherglen
(O’Reilly, paperback Kindle)

“Hive,” the three authors point out, “provides an SQL dialect, called Hive Query Language (abbreviated HiveQL or just HQL), for querying data stored in a Hadoop cluster.”

They add: “Hive is most suited for data warehouse applications, where relatively static data is analyzed, fast response times are not required, and when data is not changing rapidly.”

Their well-structured and well-written book shows how to install and test Hadoop and Hive on a personal workstation – “a convenient way to learn and experiment with Hadoop.” Then it shows “how to configure Hive for use on Hadoop clusters.”

They also provide a brief overview of Hadoop and MapReduce before diving into Hive’s command-line interface (CLI) and introductory aspects such as how to embed lines of comments in Hive v0.80 and later.

From there, the book flows smoothly into HiveQL and how to use its SQL dialect to query, summarize, and analyze large datasets that Hadoop has stored in its distributed filesystem.

User documentation for Hive and Hadoop has been sparse, so Programming Hive definitely fills a solid need. Significantly, the final chapter presents several “Case Study Examples from the User Trenches” where real companies explain how they have used Hive to solve some very challenging problems involving Big Data.

Python for Data Analysis
Wes McKinney
(O’Reilly, paperbackKindle)

No, Python is not the first language many people think of when picturing large data analysis projects. For one thing, it’s an interpreted language, so Python code runs a lot slower than code written in compiled programming languages such as C++ or Java.

Also, the author concedes, “Python is not an ideal language for highly concurrent, multithreaded applications, particularly applications with many CPU-bound threads.” The software’s global interpreter lock (GIL) “prevents the interpreter from executing more than one Python bytecode instruction at a time.”

Thus, Python will not soon be challenging Hadoop to a Big Data petabyte speed duel.

On the other hand, Python is reasonably easy to learn, and it has strong and widespread support within the scientific and academic communities, where a lot of data must get crunched at a reasonable clip, if not at blinding speed.

And Wes McKinney is the main author of pandas, Python’s increasingly popular open source library for data analysis. It (pandas) is “designed to make working with structured data fast, easy, and expressive.”

His book makes a good case for using Python in at least some Big Data situations. “In recent years,” he states, “Python’s improved library support (primarily pandas) has made it a strong alternative for data manipulation tasks. Combined with Python’s strength in general purpose programming, it is an excellent choice as a single language for building data-centric applications.”

Much of this well-written, well-illustrated book “focuses on high-performance array-based computing tools for working with large data sets.” It uses a case-study-examples approach to demonstrate how to tackle a wide range of data analysis problems, using Python libraries that include pandas, NumPy, matplotlib, and IPython, “the component in the standard scientific Python toolset that ties everything together.”

By the way, if you have never programmed in Python, check out the end of McKinney’s book. An appendix titled “Python Language Essentials” gives a good overview of the language, with a specific bias toward “processing and manipulating structured and unstructured data.”

If you do scientific, academic, or business computing and need to crunch and visualize a lot of data, definitely check out Python for Data Analysis.

You may be pleasantly surprised at how well and how easily Python and its data-analysis libraries can do the job.

Si Dunn

Hadoop: The Definitive Guide, Third Edition – Big Tools for Big Data – #programming #bookreview

Hadoop: The Definitive Guide, Third Edition
Tom White
(O’Reilly, paperback, list price $49.99; Kindle edition, list price, $39.99)

“The good news is that Big Data is here,” Tom White writes in this revised and updated third edition to Hadoop’s “definitive guide.” But: “The bad news is that we are struggling to store and analyze it.”

Indeed, Big Data is now being measured in zettabytes, which is “equivalently one thousand exabytes, one million petabytes, or one billion terabytes,” White says. And all of us are creating, storing and trying to benefit from expanding amounts of data each day.

Enter Hadoop, “a reliable shared storage and analysis system. The storage is provided by HDFS [the Hadoop Distributed File System] and the analysis by MapReduce. There are other parts to Hadoop,” White emphasizes, “but these capabilities are its kernel.”

Hadoop (it’s not an acronym; simply the name of a child’s toy elephant) is a complex programming language. But, White says: “Stripped to its core, the tools that Hadoop provides for building distributed systems—for data storage, data analysis, and coordination—are simple. If there’s a common theme, it’s about raising the level of abstraction—to create building blocks for programmers who just happen to have lots of data to store, or lots of data to analyze, or lots of machines to coordinate, and who don’t have the time , the skill, or the inclination to become distributed systems experts to build the infrastructure to handle it.”

This new edition covers recent changes and additions to Hadoop, including the MapReduce API and new MapReduce 2 runtime, “which is built on a new distributed resource management system called YARN.” Several chapters related to MapReduce and other topics also have been added or expanded.

Hadoop can run MapReduce programs written in a variety of languages, including Java, Ruby, Python, and C++. And: “MapReduce programs are inherently parallel, thus putting very large-scare data analysis into the hands of anyone with enough machines at her disposal.” Hadoop, meanwhile, provides powerful parallel processing capabilities.

Hadoop increasingly is being employed by companies and organizations that must deal with processing, analyzing, and storing very large amounts of data. White’s book includes some case studies that explain Hadoop’s role in solving several Big Data challenges.

Hadoop: The Definitive Guide, Third Edition is not a beginner’s how-to book. But it’s definitely recommended for “programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.”

Si Dunn