Big Data: What’s Love Got to Do with It?

Posted 21 November 2016 3:23 PM by Bill Wohl



Folks, it’s time to give Big Data more love, and here’s why: we know Big Data contributes to business value, but right now, could you give a summary of the Big Data protection and recovery plan you have in place?  More than likely you assume Big Data sets are getting the backup and recovery attention they deserve. But, they’re probably not.  While ‘regular’ sized data sets have enjoyed feature-rich protection solutions, Big Data, to date, has not been given the same level of support. It’s like saying you really like your whizzy sports car, but you’re only making oil changes when you get around to it.

A casual approach to Big Data puts the organization at risk of failing to meet compliance and governance regulations. As large data sets increasingly become an integral part of an organization’s information and knowledge base, and therefore subject to governance, there is more pressure on IT to ensure the data is searchable and that recovery point objectives are met. Organizations need a protection solution that is Big Data ‘aware’ so that automated disaster recovery and enhanced levels of visibility are achieved for leading Big Data platforms such as Hadoop, Greenplum and GPFS.

Deploying an effective protection and recovery solution for such large data sets has been a challenge for IT, which must balance this need against the expectation that costs will be reasonably contained. As you consider how to improve your Big Data protection, here are a few key items to ponder:

Can’t Tie it Up in a Bow: Big Data is growing and the aspect that IT fears most is unstructured data. All those Twitter feeds, Facebook posts and images, and video clips – this type of data shows no signs of slowing down. In fact, IDC estimates unstructured data to rise from 9.3 zettabytes in 2015 to 44.1 zettabytes by 2020.1 Since some of this data has value for customer insights, it does need protection. However, industry surveys show that organizations are identifying unstructured data as a secondary concern and are pretty nonchalant about their future plans for protection. 

One Big [Data] Security Umbrella: Big Data sets are part of the larger mix of data in an organization. There is also structured data and analytic data. To avoid this turning into a multi-structured mess, IT needs to look at a converged, integrated backup and archive process to ensure that all types of data can be recovered, and discovered, to meet compliance and governance demands.

Putting the Puzzle Pieces Together:  Big Data sets are often organized into multi-nodes, creating another security puzzle for IT to solve. The solution lies in software that can automate disaster recovery for multi-node systems. A single user interface can also help simplify security. IT can set rules and policies across environments, including Big Data, making recovery more efficient and costs are more controlled. This environment flexibility is imperative since large data sets may reside in the cloud, on-premises or in a virtual repository.

Big Data continues to expand. Forrester predicts the global big data management solutions market to grow at a compound annual growth rate (CAGR) of 12.8 percent from 2016 to 2021.2 The analyst firm makes a strong statement that traditional data processing applications are inadequate.

These large data sets are gaining in importance and have catapulted data analytics into another dimension of complexity and scale. It’s time to show Big Data more love by choosing protection and recovery solutions that can not only handle this level of complexity, but also deliver the level of compliance and governance security organizations need.

Learn more in the Commvault whitepaper Big Data Trainwreck Ahead! It shows how you can get a grip on the Big Data protection and management challenge to assure that you can keep governance and compliance in control.

 

1 Source: Adoption and Trends in Object-based Storage, 2015 Data Storage Innovation Conference

2 Source: Big Data Management Solutions Forecast, 2016-2021 (Global)

 

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