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

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Realize the Importance of Reliability

For organizations whose data scale extends into petabytes, reliability is crucial. Most systems seem reliable on the surface, but data loss is common when the volume approaches this higher scale. Erasure coding is often used in such instances, but the reliability comes at the expense of high streaming performance. (It also requires significant compute and networking resources.)

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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