Reducing your 'Big' Data


(S/SVR) #1

One of the biggest characteristic of Big Data, is the amount of storage you require, to store them on your premise. And as well all know, enterprise storage is the most expensive component of your data center. Ultimately, reducing your storage footprint with the help of compression, must be a default factor to consider, when you’re building a smart storage solution for your Big Data.

But, if you are looking to cut down your storage costs, this is just one of the solutions you can implement. There’s more to what you can do to run your storage infrastructure efficiently. Compressing your data not only saves you a lot of disk space, but also drastically reduces the IOPS. Reducing the IOPS is a well-known method to accelerate the performance of your overall system.

Shrink’s Server takes data compression to the next level. To begin with, Shrink organizes your unstructured data into something you can easily access and make it meaningful. And since Shrink uses a unique column based compression for each and every file, the compression rates are remarkably faster, and precise. Saving you time, money and resources.

In traditional storage warehouses, a major portion of storage can be used by indexes. But Shrink’s in-memory indexing technology, completely eliminates the need for these indexes to be stored in your arrays, thus providing a massive additional savings on your storage, maintenance and cost.

Now that the industry is going full-on with big data, one thing some companies miss out, is to consider the amount of growth their storage landscape is going to take. With Shrink, we are trying to solve this problem with exceptional technologies.


This is a companion discussion topic for the original entry at https://www.skcript.com/svr/reducing-your-big-data/