These days we tend to gather and stock tons of data from countless sources that consist of the World Wide Web (WWW), numerous social media bustles, mobile phones and many more. It is normally found that software tends to route for new and better-quality hardware, but it is not the case with Big Data. Big Data is rapidly changing the dynamics of storage hardware development & networking infrastructure and finding new ways to handle the ever-growing needs of this computer centric world. Which brings us to the most vital structural aspect of the Big Data analytic Storage.
Adequate Storage Capacity
As you must now be knowing from our previous research work, any data which goes above the size of petabyte is known as Big Data. The amount of data increases at a very swiftly and so storage of data plays an important role. The storage system needs to be vastly scalable as well as suitably flexible so that you won’t entail to bring the entire system down just to increase space. Steadily, you will find your data translating into an enormous amount of metadata, in which case a traditional file system will not support it. You can leverage object oriented file systems in order to reduce its scalability.
Big Data Latency
Big Data analytics comprises of social media activity tracking and transactional behaviors. These are used for calculate real-time decision making. Hence, Big Data storage must not look hidden otherwise it risks turning into stale data. There are some applications which may require real-time data for real-time decision making. Storage systems must be capable of scaling out without compromising on performance, which can be accomplished by deploying a flash based storage system.
Ease of Access
As Big Data analytics is utilized across several platforms and hosting systems, there is a bigger need to cross-reference data and mix all together so as to give the big picture. Also, the storage needs to handle huge data from a various sources at the same time.
High Level Security
As an outcome of cross-referencing data, new contemplations for data level safety may be essential over present IT circumstances. The storage must be able to hold such levels of data security necessities, without compromising on scalability or latency.
When you have Big Data, it will also bring big prices. Storage is also considered as the most expensive factor of Big Data analytics. However, a few simple procedures like data de-duplication, using tape for backup systems, data redundancy, building customized hardware instead of ready-made storage appliances, can considerably lower your costs.
Typically, Big Data integrates a Business Intelligence (BI) application, which needs regular data integration and on-going data migration. However, considering the scale of Big Data, the storage system needs to be fixed without any prerequisite of data migration needs. Likewise, it must be flexible enough to hold onto diverse types and sources of data. Again, this should be done without compromising on performance or latency. Utmost caution needs to be taken to envision all the potential current and future scenarios and use-cases while formulating and scheming the storage system.
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