How to Future-Proof Your Data Lake: Six Critical Considerations

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Provide Rich Metadata Support

The larger the data set gets, the more important it is that the metadata – in effect the index to the data stored in the data lake – not only be flexible and extensible, but scalable in its own right. The ability to make sense of the varied and vast set of objects in your data lake is going to depend on the capabilities of the metadata system. Without the appropriate metadata and the underlying mechanism to maintain it, you risk creating what Gartner calls a "data swamp."

We have reached an inflection point in the rate of data creation that, unless you are willing to start throwing huge quantities of it away, you simply cannot afford to continue using the same technologies and tools to store and analyze it. The existing data silos – impractical for many reasons beyond pure expense – simply must be consolidated, even if the full picture of exactly how the utility of each piece of data will be maximized is still unknown.

One potential option many businesses have chosen to pursue in the hopes of addressing current business concerns while also maximizing future possibilities and minimizing future risks is building a data lake. With that, however, comes a separate set of challenges and considerations.

Large data volumes drive the need for data lakes. In simple terms, a data lake is a repository for large quantities and varieties of data, both structured and unstructured. The data is placed in a store, where it can be saved for analysis throughout the organization. In this slideshow, Storiant, a cloud storage provider, has identified six tips on how a data lake can reconcile large volumes of data with the need to access it.


Related Topics : Fujitsu, Storage Virtualization, Desktop Virtualization, Virtual Tape Library, InfiniBand

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