Improve backup efficiency How large amounts of data solve storage challenges

As reported in many reports, a large number of newly created data has caused great concern to the information industry practitioners, especially storage managers.

The focus is on how to process the data, what data to save, and where to save it. The question also includes how to submit information, where to save the data, what are the requirements for the data and how much it costs to save it. Most new data is in the form of files.

The large increase in data is good news for storage vendors, which means more storage is needed. But for the enterprise, keeping all these new data is not continuous because of the cost and the physical space and energy consumption required by the storage system.

All of this data requires a new method for storage and archiving. Whenever new data is acquired and analysis is required, a new method needs to be established to establish data associations. Relevance means that data analysis is needed immediately. For example, data received from a data source (eg, monitoring system, feedback system, etc.) is immediately analyzed. At the same time, the relevant newly acquired source data will be saved to the backup system. Information that is temporarily reserved and valuable is stored in data analysis nodes or shared storage systems.

If needed, the backed up source data can then be processed or analyzed by data mining. The backup system can also provide the data protection process, that is, the single protection of new data according to the requirements of the business process.

Data correlation can be established through the use of a data analysis engine or through advanced features provided by the storage system. Based on the given analysis rules, the data correlation engine can store the associated data in the most suitable location. Some data will be stored in the main storage system, and the vast majority of data will be stored directly in a more economical backup system.

This may not be a new model, but it can streamline the process of managing data and the time it takes. With similar IT solutions, the economic costs can be greatly reduced and can be immediately applied to optimize the processing of a large amount of generated data.