In the trending times, big data is becoming one of the priorities for the organizations which are increasingly aware of the central role data that can play in their success. Still, the firms struggle for the protection of the data, managing and analyzing the data within present modern architectures. Not implementing so can result in extended downtime and potential data loss costing the organization high bucks.
There are various causes for data corruption such as storage device gets old, data loss, hacking, sudden loss of power etc. so what can you do to prevent your files from being corrupted? There must be a strong recovery service as provided by the big data which should be looked upon.
Reason 1: Stop replicating the data
Instead of opting for the multiple replicas of the data to eliminate the backup recovery, big data creates multiple copies of the data and distributes these copies across different servers or racks. Such data redundancy provides data protection in case of hardware failures, accidental deletions, data corruptions etc. as these errors extend to all the copies of the data.
Reason 2: Lost Data Recovery
With the help of big data, it is possible to recover the lost data only if the organization has a collection of raw data. It can take up to weeks consuming significant engineering resources and cause extended downtime. So, a raw data collection seems to be the first priority for the organization in order to proceed with the data backup and recovery methods.
Reason 3: Identify Data Subset
It can be time-consuming for a petabyte of big data and also not advisable as it is not economical or practical. It requires a large amount of investment for full periodic backups and can take up to weeks or month. Opposite to this, you can identify a data subset that is valuable for an organization and only back up that data which helps to reduce costs and speeds up the backup process.
Reason 4: Small Recovery Operation Cost
The big data backup and recovery operations do not cost much as compared to the additional cost of running scripts, debugging and performing ad-hoc recoveries by a person. It also eliminates the cost of storing backups and locate the backup copies for restoring the data when required.
Reason 5: Snapshots can work effectively
Some extra manual steps are needed to be done for the snapshot mechanism to ensure the consistency of backup data and metadata. It is very much useful when the data is not changing more rapidly which allows easy recovery by manual process. The admin will have to identify snapshot files that need to be stored in correspondence to the data and restore it in their specific node in the cluster.
Exciting times ahead
For conclusion, organizations can deploy big data platforms and applications for data backup must ensure proper data protection to minimize downtime. Proper planning and investment are required for efficient backup and recovery which is a driving factor for business value. Human error and data corruptions will happen even if you do not put an option of backup and recovery solution for big data. Every organization must gain the maximum advantage of the big data services for future growth and advanced development.
Author Bio: Kibo Hutchinson is a Business Trend Analyst at TatvaSoft UK which is a big data company in London. She strongly believes that knowledge is meant to be shared and in this post she is sharing her insights on Big Data.