How will Big Data Impact Data Recovery?

As big data becomes increasingly popular, do you wonder how it will influence data recovery? This article takes a brief review of what makes up big data and how it will impact data recovery.

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Until recently, companies had access to tonnes of unstructured data but could not use it because of the lack of analytical tools that could process it into meaning information. Now, this has changed and big data is increasingly becoming the basis for critical business decisions. How will it affect data recovery? 

A Sneak Peek at Big Data

To understand what big data is, it is wise to look at its characteristics. Essentially, this is voluminous data received from numerous sources at high speed. It comes in all sorts of formats and its consistency may vary from time to time.  Big data can be both structured and unstructured. 

For instance, shipping companies that use the internet of things (IoT) to track parcels across the globe rely on structured big data from transmitting devices to pinpoint the exact location of the cargo. On the other hand, companies receive lots of data on social media and other channels that are not structured. This could be photos, text messages, or even videos of varied formats. 

 

Perceptions About Big Data

When it comes to data backup and recovery, companies in certain vertices give priority to mission-critical data. This is because, in such industries, big data is historic in nature and does not change. In such cases, big data is not a priority. Moreover, because of its sheer size, not many of these companies are willing to allocate large disk space to big data. 

In other verticals, big data is dynamic and real-time. There are companies that rely on such data to run their daily operations. In this case, it is important to backup such information to enhance business continuity in the event of a disaster. 

Big Data and Data Recovery

The current consumption of big data shows that it is quickly becoming part of mission-critical information for many organizations. Recovering big data in case it is corrupted requires powerful tools that do not require human intervention. It will require tools such as DataNumen SQL Recovery software that can easily integrate with big data processing. This allows for easy automation of big data recovery solutions.

Such tools are fast and require skilled experts to configure. This means that the cost of recovering big data is likely to increase. Another factor to consider before starting the recovery procedure for big data is storage space. Companies will be expected to have access to large data storage facilities that can accommodate the data.

Conclusion

Big data is increasingly becoming popular among many companies. In some cases, it is part of their mission-critical applications. When recovering big data, it is important to make sure that you have sufficient storage to match the size of the corrupt data. The tools used for recovering big data are fully automated and require skilled experts. Therefore, these factors are likely to increase the cost of data recovery. As the landscape of data recovery changes, it is imperative that experts will have to acquire data science skills for them to remain relevant to the market.

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