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Code reduction geek days

code reduction geek days

failure in an Xbrick. . While the cost of nand media at various write per day (WPD) factors keeps plummeting (and will continue to drop without inline data reduction, the TCO is not as clear (in spite of all the operational, simplicity, and of course performance benefits). Now I guarantee well get a ton of vendor flack here. . What is highly dedupe-able (server images) and what is not (databases) and what is highly compressible (databases) and what is not (images) is well known. . Oh, and its an NDU upgrade for all XtremIO.0 customers. But to be clear, in our view, its not a legitimate thing to claim as part of data reduction. I would be very curious about this data from arrays in the field with media with different WPD (Write Per Day) characteristics. . It also means that writes are distributed all over a cluster. .

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For the following resources to reduce their transfer size.9KiB (73 reduction ).
We will show you, code -2- reduction value, date of creation, location, hosted server, local language and estimated data - The estimated data.

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This isnt to imply that XtremIO is not ready for mission critical of workloads. Think of Lempel-Ziv or LZ compression :-) You can see that the largest population gets between a 41-60 savings due to compression. . Heres some more stats. . And yes, in the picture, you can see these new nodes have a cool new bad-a faceplate for some of the new XtremIO cluster configs :-) 8 X-brick (16 node) cluster support. . The local team had used our Mitrend data code promo shelm reduction analysis using the customers data and while the tool suggested a 4:1 (with the customer data) was very reasonable, they were conservative, and modeled the TCO at a 3:1. . Thats a profound fact and while there are lots of marketing efforts about guarantee programs, we feel firm and safe (and have the data to support which were sharing transparently) about the life of our nand in XtremIO and as you can see, architecture matters. . It simplifies operations, and does enable customers to get more out of systems. .

Customers have voted with their feet and their dollars as they have realized scale-out is simply a better and easier way to get the most out of flash. . The chart above also shows that after the initial overwrite which takes 6 months, subsequent overwrites are taking just days. . What you CAN count on is that youre statistically likely with mixed workloads to land somewhere between 3:1 and 5:1. Some include thin provisioning (hah!). .

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