Data lakes have become a mainstream strategy for many enterprises over the past couple of years, with promises of greater flexibility in the way data is handled and made available to decision makers. A recent survey by Unisphere Research, a division of Information Today, Inc., found that 20% of data managers and professionals are currently deploying data lakes, and 45% are learning and researching about them. A majority, 56%, have a positive impression of the concept in that it may serve some value to their businesses. At least 38% indicate their companies are committed to data lake strategies (“Data Lake Adoption and Maturity Survey Findings Report,” Unisphere Research, October 2015).
In most cases, data lakes are defined as data environments that capture and store raw data. A data lake comprises data in its original format, to be transferred and transformed at a later date as applications and end users demand. The thinking behind the concept is that the analytics or questions to be applied against the data may have not yet been identified, and by holding the data in a relatively accessible environment, it is open for future innovation.
However, as with any major enterprise data initiative, the con- cept has to be sold to the enterprise. Data lakes absorb data from a variety of sources and store it all in one place, with all the nec- essary requirements for integration and security. Data lakes are a response to the eternal problem of data silos, attempting to bypass these various, fragmented environments to finally maintain data all in one place. The data lake also reduces the requirement for immediately processing or integrating the wide variety of data formats that comprise big data.
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