Companies that need to process large and varied data sets frequently look to Apache Hadoop as a potential tool, because it offers the ability to process, store and manage huge amounts of both structured and unstructured data. The open source Hadoop framework is built on top of a distributed file system and a cluster architecture that enable it to transfer data rapidly and continue operating even if one or more compute nodes fail. But Hadoop isn't a cure-all system for big data application needs as a whole. And while big-name Internet companies like Yahoo, Facebook, Twitter, eBay and Google are prominent users of the technology, Hadoop projects are new undertakings for many other types of organizations.
Some industry analysts assert that Hadoop is still in its adolescent stages, a technology that requires more development and careful examination by prospective users. The release of the second-generation Hadoop 2 software in October 2013 added broader application support and features designed to improve cluster availability and scalability. Even so, Hadoop typically isn't a one-stop-shopping product and must be used in coordination with MapReduce and a range of other complementary technologies from what is referred to as the Hadoop ecosystem.
Although it's open source, it's by no means free. Companies implementing a Hadoop cluster generally choose one of the commercial distributions of the framework, which poses maintenance and support costs, and they need to pay for hardware and hire experienced programmers or train existing employees on working with Hadoop, MapReduce and related technologies such as Hive, HBase and Pig.
For many people, big data deployments and Hadoop projects are one and the same. That isn't the case, but Hadoop clearly has a central role to play in big data management and analytics initiatives. Learn more about the Hadoop framework in this guide, which offers different perspectives on Hadoop and explains how the technology can be helpful, where it doesn't measure up and why it isn't going away any time soon.
Understanding and using Hadoop
1. Elucidating benefits, myths and facts about Hadoop
Before deciding to implement the Hadoop framework as a tool for managing and analyzing big data, IT decision makers should understand exactly what Hadoop is and how it works. In the articles in this section, experienced users and industry analysts discuss the potential benefits of Hadoop projects, dispel myths surrounding the technology and explore how using Hadoop clusters can generate a return on investment for organizations.
Hadoop's ongoing evolution
2. Keeping up with Hadoop news and trends
As with other technologies, Hadoop is continually evolving to meet shifting big data management needs and business goals. The articles in this section catalog Hadoop technology trends, offering a look at new functionality, expanding applications and supporting tools in the Hadoop ecosystem.
Hadoop issues and shortcomings
3. Examining issues and weaknesses in the Hadoop ecosystem
While many users find Hadoop projects to be cost-effective and useful, they have some drawbacks to keep in mind in assessing whether it's the right technology for an organization. In this section, users and analysts discuss where Hadoop falls short, particularly in terms of real costs, ease of management, performance and overall capability, and offer advice on how to avoid problems on deployments.
4. Analysis of Hadoop and big data technologies
Watch the video interviews in this section for analyses and insights into the issues involved in evaluating, deploying and managing Hadoop tools and big data technologies. Well-known consultants and industry analysts share tips on adoption of Hadoop and other big data tools and on how to implement successful big data management and analytics programs.
Consultant Colin White discusses the maturity of Hadoop tools and details some of the key issues to consider when evaluating Hadoop distributions.
Shawn Rogers of Enterprise Management Associates explains a common roadblock to adoption of big data systems and technologies -- a lack of big data skills in organizations.
Wayne Eckerson offers advice on using big data analytics technology and shares his view of the big data big picture.
5. Glossary of Hadoop-related terminology
This glossary offers definitions of key terms pertinent to Hadoop projects and big data initiatives.
6. Test your understanding of the Hadoop ecosystem
Take this brief quiz to see what you have learned about Hadoop.