Data deduplication, also called dedupe, is an evolutionary advancement in the field of backup technology. It’s been around for a couple of decades now and becoming increasingly valuable. The biggest problem in the IT world has been the storage of data. For the corporates and enterprises dealing with a huge employee/client base, it can be a monumental task to deal with the created data. A traditional backup solution does not provide any inherent capability to prevent duplicate data from being backed up. This, along with the growth of information and 24/7 applications requirements, significantly increases the backup window size and storage resources requirements and network bandwidth. Dedupe helps optimize the storage capacity leading to resources and cost saves.
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Deduplication is essentially a process of identifying and eliminating redundant data. It tries to shrink the amount of data stored by removing the duplicate blocks from a dataset. Dedupe technique ensures the storage of only one unique instance of data on the storage media. The data can be from different files, directories or servers. The copies of the same dataset are replaced with pointers to the unique data copy. It also aligns with the incremental backup and copies only the information that has changed since the last backup. This helps reduce the storage requirement, shortens the backup window, and reduces the network burden.
For example, an email system where the same email is shared with many users will have multiple instances of the same file attachment. If the email platform is archived using deduplication, only one instance of the attachment is stored on the storage media, and the rest of the instances are referenced back to the one saved copy. This will result in a reduction of backup storage space overhead.
Data deduplication is a technology that is used in many different ways with many different products. It works by either comparing files or creating and comparing blocks of data called chunks.
File-level deduplication, also called Single-Instance storage, compares files to be archived with the ones already stored. It detects and removes redundant copies of identical files. While storing a file, its attributes are checked against an index, if it is unique, it is stored, and if not, only a pointer (stub) is created to an existing similar file. This is simple and fast but does not address the problem of the duplicate content within the file.
Sub-file deduplication breaks the file into smaller chunks (contiguous blocks of data) and then uses a specialized algorithm to detect redundant data within and across a file. As a result, it eliminates duplicate data across files. There are 2 methods of sub-file deduplication:
There are 2 ways of data dedupe implementation.
It occurs at the backup device or the target, which offloads the backup client from the deduplication process. Data is de-duplicated at the target either immediately or at a scheduled time using 2 methods:
Because the dedupe happens at the target, all the backup needs to be transferred, requiring a high network bandwidth.
It eliminates redundant data at the source (backup client) before it transfers to the backup device. This can dramatically reduce the amount of backup data sent over the network during the backup process. The backup client sends only new and unique segments across the network. It provides the benefit of shorter backup windows, reduced storage capacity, and network bandwidth requirements.
Deduplication reduces the infrastructure cost because less storage is required to hold backup images. It enables longer retention periods owing to the reduction of the redundant content in the daily backups. It also shrinks the backup window and the backup bandwidth requirement. By eliminating the duplicate content, increases efficiency reduces time and lessens the system resources load.
There are some downsides as well to this technology. Block-level dedupe uses the hashes, which can lead to hash collisions and loss of data integrity. It also possesses some challenges while restoring the data. If the referenced data or the copy of the data stored on the storage media corrupts, then all the data it points to in the system goes bad.
Virtual cloud storage environments are widely in use for data storage. This promotes the use of data deduplication as a virtual server environment increases the efficiency of this process. This is because the possibility of finding a huge amount of duplicate data increases, which can be easily removed. Dedupe not only has noteworthy benefits when it comes to cost saves, but it also helps in improving data governance. Using the end-user ability to understand data-usage patterns can help them proactively optimize data redundancies.
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