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It should be noted, for example, that the operational framework is on version 1.0, as offered through the Apache Foundation. To date, many implementations are either more commonly seen among the large web properties or within the depths of data management or IT departments, as pilot projects or as part of efforts to optimize operations within these departments.
While enterprise adoption of Hadoop is expanding, it also brings new types of challenges.
Hadoop also requires a high degree of skill and understanding to install and implement. Hadoop implementation and management skills, as well as MapReduce development skills, are in high demand and difficult to find. As a result, enterprises seeking to employ Hadoop-based data analysis environments will either require highly trained IT and data management departments, or will need to rely on third-party consultants.
STEPS TO SUCCESS
The following are steps to success for adopting Hadoop-based big data analytics in enterprises:
Learn the technology. The Hadoop framework and ecosystem introduces new sets of solutions, such as the Hadoop Distributed File System and MapReduce Engine, along with a range of add-on applications such as Hbase, Hive, Pig, and Sqoop. There are numerous online training programs available, as well as online tutorials, webinars, books, and white papers to further acquaint enterprise teams with the features and technical details of Hadoop.
For more articles on this topic, go to DBTA’s Thought Leadership Section: Unleashing the Power of Hadoop for Big Data Analytics.
Develop a test environment to pilot Hadoop projects. Reference architecturesare now available across the industryfor various scenarios of Hadoopimplementations. This will also help in the selection of tools that will benefit users accessing the information comingfrom the Hadoop framework.
Work with the business. Hadoop may be more commonly used for optimizing internal IT or data management operations, but it is rapidly gaining ground as a strategic big data analytics platform as well. Hadoop isn’t operated in isolation, since it involves pulling in data from different systems and then publishing that data out to different systems. It’s also important to develop use cases for the big data analysis project, to map out data flows and determine what data is required. Very importantly, there has to be a demonstrated return on investment. If Hadoop won’t bring in additional revenue or cut costs for the business, it may not be worth implementing the technology. Solving the business problem is the ultimate return.
Provide and encourage training and skills development. The Hadoop platformrequires specialized skill sets that are notreadily available on the job market across several different disciplines in the data analytics space—systems administration, application development, data analysis andstewardship, and networking expertise. Manyof these skills are already resident withinorganizations—and many individuals in data management or IT departments will be able to grow into these roles.
For more articles on this topic, go to DBTA’s Thought Leadership Section: Unleashing the Power of Hadoop for Big Data Analytics.