How does data deduplication work?

The proliferation of self-storage units has exploded in recent years. These large-scale warehouse facilities have become a booming industry across the country for one reason: The average person now has more possessions than he or she knows what to do with.

The same basic situation is affecting the IT world as well. We are in the midst of an explosion of data. Now, thanks to the capabilities of the Internet of Things (IoT), even relatively simple, everyday objects routinely generate their own data. Never before in history has so much data been generated, collected, and analyzed. And never before have more data managers wrestled with the question of how to store so much data.

A company may not initially realize how big the problem can become, and then the company will have to look for more storage solutions. Over time, your company may outgrow its storage system and require more investments. Inevitably, companies will tire of this game and look for cheaper, simpler options that provide data deduplication.

Many organizations use data deduplication technology (or “deduplication”) as part of their data management systems, but not many truly understand what the deduplication process is and what its purpose is. Now, let us improve your understanding of deduplication and explain how data deduplication works.

What does deduplication do?

First, let’s clarify key terms. Data deduplication is a process that organizations use to streamline data retention and reduce the amount of data they keep by eliminating duplicate copies of data.

I should also point out that when I talk about duplicate data, I’m actually talking at the file level and referring to the rampant proliferation of data files. So when we discuss data deduplication efforts, what we really need is a file deduplication system.

What is the main goal of deduplication?

Some people have misconceptions about the nature of data, viewing it as a commodity that exists simply to be collected and harvested, like apples picked from a backyard tree.

The reality is that each new data file costs money. First of all, obtaining such data (via purchasing data lists) usually costs money. Alternatively, it requires a significant financial investment for an organization to collect and collect data on its own, even if the data is produced and collected organically by the organization itself. Data sets are therefore investments and, like any other valuable investment, must be strictly protected.

In this case, we are talking about data storage space (in the form of on-premise hardware servers or cloud storage through a cloud-based data center) that must be purchased or leased.

Therefore, redundant copies of replicated data drive up your bottom line by incurring additional storage costs on top of the costs associated with the primary storage system and its storage space. This means you need to allocate more storage media assets to accommodate both new and already stored data. At some point in a company’s operations, duplicate data can easily become a financial liability.

In summary, the primary goal of data deduplication is to save money by reducing how much money an organization spends on additional storage.

Additional benefits of deduplication

There are other reasons beyond storage capacity for companies to adopt data deduplication solutions. Perhaps nothing is more important than the data protection and enhancements your solution provides. Organizations improve and optimize their deduplicated data workloads to run more efficiently than data full of duplicate files.

Another important aspect of deduplication is how it supports quick and successful disaster recovery operations and minimizes the amount of data loss that can result from such events. Dedupe supports a robust backup process, so your organization’s backup system is left with the same job of handling backup data. In addition to helping with full backups, deduplication also aids retention efforts.

Another benefit of data deduplication is how well it works with virtual desktop infrastructure (VDI) deployments, thanks to the fact that the virtual hard disks behind the VDI remote desktop behave the same. Popular Desktop as a Service (DaaS) offerings include Microsoft’s Azure Virtual Desktop and Windows VDI. These products create virtual machines (VMs) that are created during the server virtualization process. As a result, these virtual machines power VDI technology.

Deduplication Methodology

The most commonly used form of data deduplication is block deduplication. This method works by using automated functions to identify duplicates in blocks of data and then remove those duplicates. Working at this block level allows you to analyze unique chunks of data and designate them as worthy of verification and preservation. Then, when the deduplication software detects a repetition of the same block of data, that repetition is removed and a reference to the original data is included in its place.

Although this is the main form of deduplication, it is not the only method. In other use cases, alternative methods of data deduplication operate at the file level. Single instance storage compares entire copies of data within a file server, not chunks or blocks of data. Like its counterparts, file deduplication relies on keeping the original files within the file system and removing additional copies.

It is important to note that deduplication techniques do not work in the same way as data compression algorithms (e.g. LZ77, LZ78). However, it is true that both pursue the same general goal of reducing data duplication. Deduplication techniques achieve this on a larger macro scale than compression algorithms, whose goal is to encode data redundancy more efficiently rather than replacing identical files with shared copies.

Data deduplication type

There are the following types of data deduplication: when A deduplication process occurs.

• Inline deduplication: This form of data deduplication occurs in real time as data flows within the storage system. Inline deduplication systems require less data traffic because they do not transmit or store duplicate data. This may reduce the total amount of bandwidth required by your organization.
• Post-duplication deduplication: This type of deduplication occurs after data has been recorded and placed on a specific type of storage device.

It is worth explaining here that both types of data deduplication are affected by the hash calculations inherent in data deduplication. These cryptographic calculations are essential for identifying recurring patterns in data. During inline deduplication, these calculations are performed immediately, which can dominate and temporarily overwhelm the computer’s capabilities. Post-processing deduplication allows hash calculations to be performed at any time after data has been added in a way that does not place an undue burden on an organization’s computer resources.

The subtle differences between deduplication types don’t end there. Another way to classify deduplication types is based on: where Such a process occurs.

• Source deduplication: This form of deduplication occurs close to where new data is actually created. The system will scan the area, detect new copies of the files, and then remove them.
• Target deduplication: Another type of deduplication is like the inversion of source deduplication. In targeted deduplication, the system deduplicates all copies found in areas other than where the original data was created.

Because there are so many different types of deduplication implemented, forward-thinking organizations must make careful and thoughtful decisions about the type of deduplication they choose, balancing the method with their company’s specific needs.

In many use cases, the deduplication method an organization chooses can depend on a variety of internal variables, including:

• Number and types of data sets created
• Your organization’s primary storage system
• Virtual environment in use
• Apps your company relies on

Recent Data Deduplication Developments

As with all computer output, data deduplication is poised for increased use of artificial intelligence (AI) as it continues to evolve. Dedupe will become increasingly sophisticated as it develops more nuances to help find redundancy patterns as it scans blocks of data.

One of the new trends in deduplication is reinforcement learning. It uses a reward and punishment system like reinforcement training and instead applies an optimal policy for splitting or merging records.

Another notable trend is the use of ensemble methods. This method uses different models or algorithms together to ensure higher accuracy within the deduplication process.

A continuing dilemma

The IT world is increasingly obsessed with the ongoing problem of data proliferation and what to do about it. Many businesses find themselves in the awkward position of wanting to keep all the data they’ve accumulated while simultaneously trying to keep the flood of new data out of the way in every storage container they can.

While this dilemma persists, the emphasis on data deduplication efforts will continue as organizations view deduplication as a cheaper alternative to purchasing more storage. Ultimately, we intuitively understand that businesses need data, but we also know that very often data needs deduplication.

Find out how IBM Storage FlashSystem can help you with your storage needs.