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Today’s most successful companies are the ones with the ability to capture and analyze all data availableto them. Successful and efficient use of big data, after all, can allow improved decision making across thespectrum, from huge strategic questions like “Should we enter this new market?” to fine-grained, execution-oriented questions like “What should this SKU be priced?” However, as is widely known, the sheer amount of data any given company can produce has become unmanageable and therefore not valuable to businesses. Hadoop promised to alleviate this problem. To some degree it has, but the framework has also spawned many subtle issues that were not initially foreseen. A lot of companies found it relatively easy to get their data into Hadoop but hard to get that data out and use it. This enormous knowledge gap has prompted an avalanche of vendors to offer SQL-on-Hadoop solutions, which increase the accessibility of Hadoop and allow organizations to reuse their investment in SQL, widely known by most business analysts. 

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