Data Deduplication, also known as Intelligent Compression or Single-Instance Storage, is a method of reducing storage overhead by eliminating redundant copies of data. Data Deduplication techniques ensure that on storage media such as discs, flash, tape, etc., only one unique instance of data is kept. A pointer to the unique data copy replaces redundant data blocks. Data Deduplication therefore closely resembles incremental backup, which transfers just the data that has changed since the last backup.
Here’s all you need to know about Data Deduplication, as well as some key pointers to keep in mind before you start the process.
Table of Contents
- What is Data Deduplication?
- How does Data Deduplication Work?
- What is the Importance of Deduplicating Data?
- What are the Data Deduplication Ratios & Key Terms?
- Data Deduplication Use Cases: Where can it be useful?
- What are the Methods/Types of Data Deduplication Approaches?
- Data Deduplication vs Thin Processing vs Compression: What is the Difference?
- Block vs File-level Data Deduplication: What sets them apart?
- What are the advantages of Data Deduplication?
What is Data Deduplication?
Data Deduplication, or Dedup for short, is a technology that can help lower the cost of storage by reducing the impact of redundant data. Data Deduplication, when enabled, maximizes free space on a volume by reviewing the data on the volume and looking for duplicated portions. Duplicated portions of the dataset of a volume are only stored once and (optionally) compacted to save even more space. Data Deduplication reduces redundancy while maintaining Data Integrity and Veracity.
How does Data Deduplication Work?
Data Deduplication eliminates duplicate data blocks and stores only unique data blocks at the 4KB block level within a FlexVol volume and across all volumes in the aggregate. Data Deduplication relies on fingerprints, which are unique digital signatures for all 4KB data blocks. The Inline Deduplication Engine examines the incoming blocks, develops a fingerprint, and stores the fingerprint in a hash store when data is written to the system (in-memory data structure). A lookup in the hash store is conducted once the fingerprint is calculated. The data block matching to the Duplicate Fingerprint (Donor Block) is examined in cache memory when a fingerprint match is found in the hash store:
- If a match is detected, a byte-by-byte comparison is performed between the current data block (receiver block) and the donor block as verification. The recipient block is shared with the matching donor block during verification without the recipient block being written to the disc. To keep track of the sharing details, just the metadata is updated.
- If the donor block is not found in cache memory, it is prefetched from the disc into the cache and compared byte-by-byte to ensure an exact match. Without actually writing to the disc, the recipient block is flagged as a duplicate during verification. To keep track of sharing details, metadata is updated.
In the same way, the background deduplication engine functions. It searches all of the data blocks in bulk and removes duplicates by comparing block fingerprints and performing a byte-by-byte comparison to eliminate false positives. This method also assures that no data is lost during the Deduplication process.
What is the Importance of Deduplicating Data?
- Data Deduplication is crucial because it decreases your storage space requirements, saving you money and reducing the amount of bandwidth used to move data to and from remote storage sites.
- Data Deduplication can reduce storage requirements by up to 95% in some circumstances, while your specific Deduplication Ratio can be influenced by factors such as the type of data you’re attempting to deduplicate.
- Even if your storage requirements are decreased by less than 95%, Data Deduplication can save you a lot of money and boost your bandwidth availability significantly.
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What are the Data Deduplication Ratios & Key Terms?
A Data Deduplication Ratio is the comparison of the data’s original size and its size after redundancy is removed, as previously stated. It’s basically a metric for how effective the Deduplication procedure is. Given that much of the redundancy has already been eliminated, the Deduplication procedure produces comparatively poorer outcomes as the Deduplication ratio rises. A 500:1 Deduplication Ratio, for example, is not much better than a 100:1 Deduplication Ratio—in the former, 99.8% of data is deleted, whereas, in the latter, 99% of data is eliminated.
The following factors have the greatest impact on the Deduplication Ratio:
- Data Retention: The longer data is kept, the more likely it is that redundancy will be discovered.
- Data Type: Certain types of files are more prone than others to contain a high level of redundancy.
- Change Rate: If your data changes frequently, your Deduplication Ratio will most likely be lower.
- Location: The broader the scope of your Data Deduplication operations, the more likely duplicates may be discovered. Global deduplication over numerous systems, for example, usually produces a greater ratio than local Deduplication on a single device.
Data Deduplication Use Cases: Where can it be useful?
General Purpose File Servers
General-purpose File Servers are file servers that are used for a variety of purposes and may hold any of the following sorts of shares:
- Shared by the entire group
- Home folders for users
- Folders for work
- Shares in software development
Since multiple users tend to have many copies or revisions of the same file, general-purpose file servers are a strong choice for Data Deduplication. Since many binaries remain substantially unchanged from build to build, Data Deduplication benefits software development shares.
Virtual Desktop Infrastructure (VDI) deployments
Virtual Desktop Infrastructure (VDI) Deployments: VDI servers, such as Remote Desktop Services, offer a lightweight way for businesses to supply PCs to their employees. There are numerous reasons for a company to use such technology:
- Application deployment: You can deploy applications throughout your entire organization rapidly. This is especially useful when dealing with apps that are regularly updated, rarely utilized, or difficult to administer.
- Application consolidation: It eliminates the requirement to update the software on client computers by installing and running them from a group of centrally controlled virtual machines. This option also minimizes the amount of bandwidth necessary to access programs on the network.
- Remote Access: Users can access enterprise programs via remote access from devices such as personal computers, kiosks, low-powered hardware, and operating systems other than Windows.
- Branch office access: VDI deployments can improve the performance of applications for branch office workers who need access to centralized data repositories. Client/server protocols for data-intensive applications aren’t always designed for low-speed connections.
Since the virtual hard discs that drive the remote desktops for users are virtually identical, VDI deployments are excellent candidates for Data Deduplication. Additionally, Data Deduplication can help with the so-called VDI boot storm, which is when a large number of users sign in to their desktops at the same time to start the day.
Virtualized backup apps, for example, are Backup Targets. Owing to the large duplication between backup snapshots, backup programs like Microsoft Data Protection Manager (DPM) are great candidates for Data Deduplication.
What are the Method/Types of Data Deduplication Approaches?
When data is written to storage, Inline Deduplication occurs. The Deduplication Engine tags the data progressively while it is in motion. While this method is effective, it does result in additional computing overhead. The system must tag incoming data on a regular basis and then quickly determine whether or not that new fingerprint matches anything in the system. A flag pointing to the existing tag is written if this is the case. If it doesn’t, the block will be preserved as-is. Inline Deduplication is a common feature on many storage systems, and while it does add overhead, it’s not too bad because the benefits outweigh the costs.
When all data is written completely, Post-Process Deduplication, also known as Asynchronous Deduplication, occurs. The Deduplication System runs through and tags all new data removes multiple copies and replaces them with flags pointing to the original data copy at regular intervals. Businesses can use their Data Reduction service without worrying about the repetitive processing overhead produced by Inline Deduplication when using Post-Process Deduplication. This method allows organizations to schedule Deduplication so that it can take place during non-business hours.
The most significant disadvantage of Post-Process Deduplication is that all data is stored in its entirety (often called fully hydrated). As a result, the data takes up the same amount of space as non-deduplicated data. Size reduction occurs only when the scheduled Deduplication operation has been completed. Businesses that use post-process dedupe must have a bigger storage capacity overhead at all times.
Before sending data to a backup target, Source-based Deduplication removes redundant blocks at the client or server level. There is no need for any additional gear. Deduplicating data at the source saves time and space.
Backups are sent via a network to disk-based hardware present in a remote location with Target-based Deduplication. Deduplication targets raise expenses, but they usually provide a performance advantage over source deduplication, especially for petabyte-scale data sets.
Client-side Data Deduplication is a Data Deduplication technique that is used on a backup-archive client. For example, to remove redundant data during backup and archive processing before the data is sent to the server. The amount of data delivered across a local area network can be reduced by using Client-side Data Deduplication.
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Data Deduplication vs Thin Processing vs Compression: What is the Difference?
Compression is another approach frequently associated with Deduplication. Data dedupe, on the other hand, looks for duplicate data chunks, whereas compression utilizes a method to minimize the number of bits required to represent data. When data comes to Deduplication, Compression and Delta Differencing are frequently used. When combined, these three data reduction strategies are intended to maximize storage capacity.
In a storage area network, thin provisioning maximizes capacity use. Erasure coding, on the other hand, is a Data Protection Strategy that divides data into fragments and encodes each fragment with redundant data to aid in the reconstruction of corrupted data sets.
Deduplication also has the following advantages:
- A smaller data footprint;
- less bandwidth used while copying data for remote backups, replication, and disaster recovery;
- Longer periods of retention;
- Reduced tape backups and faster recovery time targets
Block vs File-level Data Deduplication: What sets them apart?
Data Deduplication is often done on a file or block level. File Deduplication removes duplicate files, although it is not a very effective method of doing it. Data Deduplication at the file level compares a file that needs to be backed up or archived with copies that already exist. This is accomplished by comparing its attributes to an index. If the file is unique, it is saved and the index is changed; otherwise, only a link to the existing file is saved. As a result, only one copy of the file is saved, with subsequent copies being replaced with a stub pointing to the original.
Block-level Deduplication searches a file for unique iterations of each block and preserves them. All of the blocks are separated into pieces of the same length. A hash algorithm, such as MD5 or SHA-1, is used to process each block of data. This method assigns each component a unique number, which is then placed in an index.
Even if only a few bytes of the content or presentation have changed, when a file is updated, only the altered data is saved. The modifications do not result in a completely new file. Block Deduplication is much more efficient as a result of this behavior. Block Deduplication, on the other hand, requires more processing power and makes use of a much larger index to track the individual bits.
Variable-length Deduplication is an approach that divides a file system into pieces of varying sizes, allowing for better data reduction ratios than fixed-length blocks. The disadvantages are that it generates more metadata and is slower. With Deduplication, hash collisions can be a problem. When a piece of data is assigned a hash number, that number is compared to the index of other Hash numbers that already exist. If the hash number already exists in the index, the data is considered redundant and does not need to be saved again. Otherwise, the index is updated with the new Hash number, and the new data is saved.
The Hash algorithm can produce the same hash number for two separate chunks of data in Rare Instances. When there is a Hash collision, the system will not save new data since the Hash number already exists in the index. A false positive is what this is known as, and it can lead to data loss. To lessen the chance of a Hash collision, some suppliers combine Hash algorithms. Metadata is also being examined by some providers in order to identify data and avoid collisions.
What are the advantages of Data Deduplication?
There is far too much redundancy in Backup Data, especially in full backups. Despite the fact that incremental backups only back up modified files, some redundant data blocks are invariably included. That’s when Data Reduction technology like this really shines. A Data Deduplication device can help you locate duplicate files and data segments within or between files, or even within a data block, with storage requirements that are an order of magnitude lower than the quantity of data to be saved.
Continuous Data Validation
There is always a risk associated with logical consistency testing in a primary storage system. The block pointers and bitmaps can be corrupted if a software bug causes erroneous data to be written. If the file system is storing backup data, faults are difficult to identify until the data is recovered, and there may not be enough time to repair errors before the data is recovered.
Higher Data Recovery
The Backup Data Recovery service level is an indicator of a backup solution’s ability to recover data accurately, quickly, and reliably. Complete Backups and Restore are faster than incremental backups because incremental backups frequently scan the entire database for altered blocks of data, and when recovery is required, one full backup and numerous incremental backups must be used, which slows down recovery.
Backup Data Disaster Recovery
For backup data, Data Deduplication has a good capacity optimization capability; doing a full backup every day requires only a small number of disc increments, and it is the data after capacity optimization that is transmitted remotely over WAN or LAN, resulting in significant network bandwidth savings.
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