Early adopters of Apache Hadoop, including high-profile users such as Yahoo, Facebook and Google, had to rely on the partnership of the Hadoop Distributed File System (HDFS) and the MapReduce programming and resource management environment. Together, those technologies enabled users to process, manage and store large amounts of structured, unstructured and semi-structured data in Hadoop clusters.
But there were limitations inherent in the Hadoop-MapReduce pairing. For example, Yahoo and other users have cited issues with the first generation of Hadoop technology not being able to keep pace with the deluge of information they're collecting online because of MapReduce's batch processing format.
Hadoop 2, an upgrade released by the Apache Software Foundation in October 2013, offers performance improvements that can benefit related technologies in the Hadoop ecosystem, including the HBase database and Hive data warehouse. But the most notable addition in Hadoop 2 -- which originally was referred to as Hadoop 2.0 -- is YARN, a new component that takes over MapReduce's resource management and job scheduling duties. YARN (short for Yet Another Resource Negotiator) enables users to deploy Hadoop systems without MapReduce. Running MapReduce applications is still an option, but other kinds of programs can now be run natively as well -- for example, real-time querying and streaming data applications. The enhanced flexibility opens the door to broader uses for big data and Hadoop projects; in addition, YARN allows users to consolidate multiple Hadoop clusters into one system to lower costs and streamline management tasks. The upgrades in Hadoop 2 also boost cluster availability and scalability, two other issues that held back the first version of the framework.
Even with the added capabilities, Hadoop 2 still has a long way to go in moving beyond the early adopter stage, particularly in mainstream IT shops. But the new version heralds a maturing technology and a revamped concept for developing and implementing big data applications. This guide explores the features of Hadoop 2 and potential new uses for Hadoop tools and systems, with insight and advice from experienced users as well as industry analysts and consultants.
1Understanding and using Hadoop-
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 projects can present managers with unfamiliar challenges -- particularly because many organizations still lack experience with the framework. Continue Reading
Data scientists and software engineers at genealogy information provider Ancestry.com worked together to employ Hadoop to support a DNA matching application. Continue Reading
Solutionary Inc. is using a system based on Hadoop and its companion HBase database to help manage log data that it combs through in an effort to detect network security threats. Continue Reading
In a panel discussion at the Hadoop Summit 2013, several enterprise users offered advice on putting Hadoop systems into action in real business applications. Continue Reading
Hadoop helps IT teams looking to efficiently mix pools of big data with the information stored in enterprise data warehouses. Continue Reading
2Hadoop's ongoing evolution-
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.
The first version of Hadoop was primarily limited to running MapReduce batch-processing jobs. Hadoop 2 supports more applications, but users still face deployment challenges. Continue Reading
Hadoop systems are provoking changes in traditional data warehouse environments. And they might also alter the status quo for mainframe modernization and migration efforts. Continue Reading
Hadoop and the open Lucene search engine are increasingly being paired up by software vendors in an effort to improve users' ability to search for information in pools of big data. Continue Reading
Yahoo's combination of Hadoop's YARN resource manager and the Storm event processor highlights the gap between enterprise needs and those of large Internet companies. Continue Reading
The need for tools that can help manage Hadoop clusters is increasing as more users move beyond experiments and deploy the open source framework in real applications. Continue Reading
Hadoop vendors are trying to clear the way for increased adoption of the technology by offering add-ons that target issues keeping some corporate users from moving forward. Continue Reading
EMC and Intel entered the Hadoop ring by releasing distributions of the software in 2013, increasing the number of choices for prospective users to contend with. Continue Reading
3Hadoop issues and shortcomings-
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.
Software vendors are adding query engines that run on top of Hadoop in an effort to turn it into a real-time data analysis platform. But some roadblocks remain. Continue Reading
While various issues can bog down the performance of Hadoop systems, there are ways to steer clear of the pitfalls and ensure that your big data applications keep cruising along. Continue Reading
There's no shortage of hoopla about Hadoop, but it isn't the answer to all big data application needs. Smart companies need to make sure it's a good match for their requirements. Continue Reading
Developers and data architects building operational business intelligence applications may need to create fast messaging infrastructures to handle streams of Hadoop data. Continue Reading
Hadoop isn't a magic bullet for meeting big data needs, says Gartner analyst Doug Laney, who offers advice on how to reap the benefits of big data investments. Continue Reading
A panel of technology vendors and analysts weighs in on the upsides and downsides of technologies such as Hadoop and MapReduce. Continue Reading
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.
TechTarget analyst Wayne Eckerson discusses the potential benefits and challenges of using Hadoop systems to run big data applications.
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.
Consultant William McKnight discusses big data basics, offering practical advice on key issues related to big data management and analysis.
Wayne Eckerson offers advice on using big data analytics technology and shares his view of the big data big picture.
Glossary of Hadoop-related terminology
This glossary offers definitions of key terms pertinent to Hadoop projects and big data initiatives.
Test your understanding of the Hadoop ecosystem
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