Top 5 Challenges for Hadoop MapReduce in the enterprise

White Papers > Top 5 Challenges for Hadoop MapReduce in the enterprise

Reporting and analysis drives businesses in making the best possible decisions. The source of all these decisions is the data.  There are two types of data: structured and unstructured. Most
recently, IT has struggled to deliver timely analysis through data warehousing architectures designed for batch processing. And these same architectures are now starting to fail under the
load of rapidly rising data volumes and new data types that beg for a continuous approach to data processing. We will review the top 5 challenges for Hadoop Mapreduce in the enterprise.

IT organizations need to adopt new ways to extract and analyze data. While existing data warehouses were built for structured data, unstructured data does not fit into the architectural mold. They need to break away from the structured data warehouse architectures of the past for the unstructured data because not all data can be molded to the structure, and there is too much of it. Moving and modifying huge volumes of unstructured data can be too costly (or time consuming) to convert it into the necessary mold for extraction. To meet emerging business demands, they need a new way to access, process, and analyze multiple types of unstructured data and their associated architectures, and it needs to be done using the same high enterprise-class standards. Without a flexible and enterprise-class approach to ultimately be able to make intelligent business decisions from the access, processing, and analysis of unstructured data, IT organizations will be overrun with data that will have no intrinsic value—the ‘big data’ problem.

Current market conditions and drivers According to the Market Strategy and BI Research group, data volumes are doubling every year:
• 42.6 percent of respondents are keeping more than three years of data for analytical purposes.
• New sources are emerging at huge volumes, in different industries, such as utilities.
• 80 percent of data is unstructured and not effectively used in the organization.

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