Harvesting the Big Data Tsunami: The Path Beyond Hadoop for Big Data Analytics

Tuesday Oct 23rd 2012
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Mike Lamble examines the current big data landscape and focuses on the analytics needed to make these exabytes actionable. Today’s DBMS for big data analytics must meet specific requirements that are outlined in this article.

By Mike Lamble, President of XtremeData

Organizations are drowning in a sea of Big Data--data that is measured in exabytes and beyond--demanding a storage platform that is expansive, economical, and accessible.

Why would a business want to store all of this big data? Because of the game-changing insights that it can yield. 85% of executives surveyed by Harvard Business Review expect to gain substantial business and IT advantages.

Hadoop's schema-free orientation makes it well suited for storing vast quantities of data in the most granular forms. It provides affordable storage on large data volumes, and enables fast data ingest.

Hadoop tools are marginal for big data integration and analytics of structured data. Slow query times, obscure access methods, data subject areas that are isolated from each other, and a lack of third-party tools for analysis, reporting and presentation disappoint the business intelligence community. Compared to Hadoop solutions, the big data DBMS solutions have fast query response times and make it easy to join disparate data. SQL skills are ubiquitous, and the third party tool ecosystem is robust.

New requirements in the DBMS analytics niche are tightly coupled with proprietary hardware, but a new, emerging class of DBMS providers is meeting these new requirements.

Read the full article at Database Journal

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