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Disasters & Data: What You Need To Know

Data DR need to be considered past simple infrastructure business continuity planning

If you’re doing business in this era, then you’re dealing with a lot of data. That data is everywhere as well, especially if parts of your business are global. Depending on where you’re doing business, the data your business is generating has to adhere to different data rules, especially where disaster recovery and business continuity are concerned.

Multiple Approaches, Multiple Concerns
There are two ways to look at big data: it either means you have a lot of data, or it means that you’re doing a lot of data processing. Often times, you’re doing both.

Transfer of documents.

Data DR need to be considered past simple infrastructure business continuity planning.

Often data is considered secondary (and thus not important), generated out of their primary application, and the company is using it to better understand how an application is working. Generally in cases such as this, the emphasis to protect data is not top priority. All businesses in this situation want are service credits towards when the infrastructure supporting data is down.

Meanwhile, others are using data processing as part of their mission-critical, revenue generating work, so the data becomes a goal in its own right. Some businesses that approach data this way are advertising, natural language processing, finance, and healthcare, among other primary forms of data analytics as well as data gathering focused businesses.

Geo-diversity Challenges
More often than not, the real solution for ensuring that data is safe is to find a storage system that is geographically replicated at the time of data creation. With big data, we are typically talking about petabytes of data. Moving that much data, however, is still a major hassle.

When you’re dealing with that level of volume, you want storage systems that are meshed, with asynchronous consistency between regions. You want to avoid needing to restore 10 petabytes of data from backup tapes. It’s a nearly impossible task. Remember, data isn’t backed up per say, but rather it’s replicated in the storage system you’re using.

Coupling that data with processing power is another issue. If you’re processing that much data, you have a server farm doing it, and it needs to be proximal to the data for it to be effective. Whether you’re using Hadoop, Oracle, or SQL warehouse tools, but something needs to be done with the data. If you have proper geographic replication of the data then typically you look to a provider based solution as preferable: something close to the data in each facility but you only pay for one at a time.

The Difficulty With Data
Something else to consider is that data is fairly immutable: it grows, but the originating data is unchanging. If there is a failure, a company has to start from the last known batch ending to reprocess. Maybe that represents a loss of 12 or 6 hours, or perhaps it’s just 10 minutes. Those numbers have to be defined to the point where they make sense in terms of impact to business continuity, since such determinations affects how you do batch data processing.

How does your business handle big data disaster recovery in the cloud? Let us know @CloudGathering.

By Jake Gardner

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