What Is Data Deduplication? Methods and Benefits

The data deduplication process systematically eliminates redundant copies of data and files, which can help reduce storage costs and improve version control. In an era when every device generates data and entire organizations share files, data deduplication is a vital part of IT operations. It’s also a key part of the data protection and continuity process. When data deduplication is applied to backups, it identifies and eliminates duplicate files and blocks, storing only one instance of each unique piece of information. This not only can help save money but also can help improve backup and recovery times because less data needs to be sent over the network.

What Is Data Deduplication?

Data deduplication is the process of removing identical files or blocks from databases and data storage. This can occur on a file-by-file, block-by-block, or individual byte level or somewhere in between as dictated by an algorithm. Results are often measured by what’s called a “data deduplication ratio.” After deduplication, organizations should have more free space, though just how much varies because some activities and file types are more prone to duplication than others. While IT departments should regularly check for duplicates, the benefits of frequent deduplication also vary widely and depend on several variables.

Key Takeaways

• Data deduplication is the process of scanning for and eliminating duplicate data.
• Deduplication tools offer a range of precision levels, from file-by-file to file segment or block dedupe.
• The more precise a deduplication process, the more compute power it requires.
• For backups and archiving, deduplication can take place before or after data transfer. The former uses less bandwidth, while the latter consumes more bandwidth but fewer local resources.

Data Deduplication Explained

In the data deduplication process, a tool scans storage volumes for duplicate data and removes flagged instances. To find duplicates, the system compares unique identifiers, or hashes, attached to each piece of data. If a match is found, only one copy of the data is stored, and duplicates are replaced with references to the original copy.

The dedupe system searches in local storage, in management tools such as data catalogs, and in data stores and scans both structured and unstructured data. To fully understand what’s involved, the following terms and definitions are key:

• Data deduplication ratio: A metric used to measure the success of the deduplication process. This ratio compares the size of the original data store with its size following deduplication. While a high ratio indicates an effective process, variables such as frequency of deduplication, type of data, and other factors can skew the final ratio. Virtualization technology, for example, creates virtual machines that can be backed up and replicated easily, providing multiple copies of data. Keeping some copies is important for redundancy and to recover from data loss.
• Data retention: The length of time that data is kept in storage, usually defined by policy. Financial reports must be kept longer than, say, emails. Typically, the longer the retention span, the greater the chance that data will be duplicated during backups, transfers, or through the use of virtual machines.
• Data type: The format of data kept in storage. Typical data types are executables, documents, and media files. The file’s purpose, criticality, access frequency, and other factors define whether it’s duplicated and how long it’s retained.
• Change rate: A metric measuring the frequency with which a file is updated or changed. Files with higher change rates are often duplicated less frequently.
• Location: The place data is stored. Duplicate files often stem from the same exact files existing in multiple locations, either intentionally, as with a backup, or unintentionally via a cut-and-paste process that accidentally used a copy-and-paste operation. In some cases, virtual machines stored in multiple locations contain duplicate files.

Why Is Data Deduplication Useful?

Data deduplication can help save resources—storage space, compute power, and money. At its most basic, deduplication is about shrinking storage volumes. But when every device produces massive amounts of data and files are constantly shared among departments, the impact of duplicate data has far-reaching consequences; for example, it can slow processes, consume hardware resources, create redundancies, and add confusion when different teams use different redundant files. Deduplication can help take care of all this, which is why many organizations keep it on a regularly scheduled cadence as part of their IT maintenance strategies.

When to Use Data Deduplication

Because data deduplication is a resource-intensive data management process, timing should depend on a number of variables, including the design of the network and when employees access files. The following are the most common situations where data deduplication is used:

General-purpose file servers

General-purpose file servers provide storage and services for a wide variety of data, including individual employees’ caches of files and shared departmental folders. Because these types of servers often have both a high volume of users and a diversity of user roles, many duplicate files tend to exist. Causes include backups from local hard drives, app installations, file sharing, and more.

Virtual desktop infrastructure (VDI) deployments

Virtual desktop infrastructure technology provides centralized hosting and management of virtualized desktops for remote access. The issue is, virtual hard drives are often identical, containing duplicate files that eat up storage. In addition, when a high volume of users boot up their virtual machines all at once, such as at the start of the workday, the ensuing "VDI boot storm" can grind performance to a crawl, if not a halt. Deduplication can help assuage this by using an in-memory cache for individual application resources as they’re called on demand.

Storage systems and backups

Backups create duplicate versions of files, for good reason. However, the same file doesn’t need to be copied over and over in perpetuity. Instead, data deduplication ensures there’s a clean backup file, with other instances in newer backup versions simply pointing to the primary file. This allows for redundancy while also optimizing resources and storage space.

Data transfers

Deduplication tools make for a more efficient data transfer process. Instead of doing a start-to-finish overwrite, data deduplication tools identify files in segments. For the file transfer process, the tools scan for updated segments and move segments only as necessary. For example, if someone is receiving a new version of a very large file and the new version has just a few segments of updated code, the transfer/overwrite process can complete quickly by writing only to those segments.

Archival systems

Archival systems are often confused with backups as they’re both used for long-term data storage. But while systems generate backups for the purposes of disaster recovery and preparedness, organizations use archival systems to preserve data that’s no longer in active use. Duplicates may be generated when combining storage volumes or adding new segments to an archival system. The deduplication process maximizes the efficiency of archives.

How Data Deduplication Works

From a big-picture perspective, data deduplication tools compare files or file blocks for duplicate identifying fingerprints, also known as hashes. If duplicates are confirmed, they’re logged and eliminated. Here’s a closer look at the specific steps in the process.

Chunking

Chunking refers to a deduplication process that breaks files down into segments, aka chunks. The size of these segments can be either algorithmically calculated or set using established guidelines. The benefit of chunking is that it allows for more precise deduplication, though it requires more compute resources.

Hashing

When data is processed by a deduplication tool, a hashing algorithm assigns a hash to it. The hash is then checked to see if it already exists within the log of processed data. If it already exists, the data is categorized as duplicate and deleted to free up storage space.

Reference tables

The results of the deduplication process are stored in a reference table that tracks which segments or files are removed and what they duplicated. The reference table allows for transparency and traceability while also providing a comprehensive archive of what sources a file referenced across a storage volume.

Data Deduplication Approaches

Organizations can choose from several data deduplication approaches based on what best suits their budgets, bandwidth, and redundancy needs. Where to process, when to process, how finely to process—all of these are mix-and-match variables that are used to create a customized solution for an organization.

Deduplication methods

• Block-level deduplication: Deduplication tools work on the block level by comparing these segments for differences in block fingerprints and removing duplicates. This allows for more precise deduplication, though the process is fairly resource intensive and may be difficult to apply to large volumes of physical storage.
• Variable-length deduplication: Variable-length deduplication uses an algorithm to determine the size of data segments in a file, then check for duplicates. This process is similar to block-level deduplication in that it offers good precision but without the fixed size of individual blocks.
• File-level deduplication: Instead of performing deduplication on a block level, tools look to detect duplicates on a file-by-file basis. This method doesn’t work with the same granularity as block-level deduplication, though the trade-off is a faster, less-resource-intensive process that can be applied to storage of any size.

Deduplication points

• Source deduplication: This method uses the local client as the location for deduplication. Performing deduplication at the client prior to backup saves on bandwidth and transmission costs, though it uses up the client’s resources.
• Target deduplication: This method waits until after a backup is transmitted to perform deduplication. In this case, the trade-off in resource use is the opposite of the trade-off for source deduplication: It puts less pressure on clients but places greater demand on network bandwidth and target resources.

Deduplication timing

• Inline deduplication: When deduplication is performed inline, data is scanned for duplicates in real time as the process executes. This method uses more local compute resources, though it frees up significant storage space.
• Post-process deduplication: Post-process deduplication runs compare-and-eliminate processes after data is sent to the target. This method requires more storage space in the target location but uses fewer local resources prior to transmission.

Benefits of Data Deduplication

Just as editing a document removes repetitive words or phrases to make the content more concise, deduplication streamlines an organization’s data, offering potential payoffs such as lower storage costs, less bandwidth consumption, and increased backup efficiency.

Storage savings

When fewer files exist, organizations use less storage. That’s one of the most clear-cut benefits of data deduplication, and it extends to other systems. Companies will require less space for backups and consume fewer compute/bandwidth resources for scanning and backing up data.

Disaster recovery

Because data deduplication reduces the burden of running backups, a key by-product is faster, easier disaster recovery. Smaller backups are created more efficiently, which means fewer resources are required to pull them for recovery purposes.

Smaller backup windows

With data deduplication, the footprint of backup files shrinks, leading to lower resource use during backup processes across storage space, compute, and process time. All this gives organizations added flexibility in how they schedule their backups.

Network efficiency

The fewer the files that need to transfer, the less bandwidth required, meaning the transfer uses fewer network resources. Thus, data deduplication can improve network efficiency by shrinking demand in any transfer process, including transporting backups for archiving and recalling backups for disaster recovery.

Economic benefits

Exploding data volumes have led to a rapid increase in storage spending in organizations of all sizes. Deduplication can help create cost savings by reducing the amount of storage needed for both day-to-day activities and backups or archives. Secondary cost savings come from reduced energy, compute, and bandwidth demands and fewer human resources needed to manage and troubleshoot duplicative files.

Data Deduplication Drawbacks and Concerns

Data deduplication is an effective tool to maximize resource use and reduce costs. However, those benefits come with some challenges, many related to the compute power required for granular dedupe. The most common drawbacks and concerns related to data deduplication include the following:

Performance overhead

Data deduplication is resource intensive, especially when performed at the block level. IT teams need to be thoughtful when scheduling and executing deduplication processes, taking into consideration available bandwidth, organizational activities and needs, the backup location, deadlines, and other factors based on their unique environments.

Hash collisions

Hash collisions refer to instances when randomly generated hash values happen to overlap. When the deduplication process uses a block-level approach, hashes are assigned to data chunks, which raises the possibility of hash collisions that can corrupt data. Preventing hash collisions involves either increasing the size of the hash table or implementing collision resolution methods, such as chaining or open addressing. Chaining involves storing multiple elements with the same hash key in a linked list or another data structure, while open addressing involves finding an alternative location within the hash table to store the duplicate element. Each method has advantages and disadvantages, so IT teams need to consider the length and complexity of the hashing algorithm versus using workarounds.

Data integrity

No process is foolproof, and during the dedupe process, there’s always the possibility of unintentionally deleting or altering data that is, in fact, unique and important. Causes of integrity issues include hash collisions; corrupted source blocks; interrupted processes from unexpected events such as disk failures, manual error, or power outages; a successful cyberattack; or simple operator error. While integrity issues are rare given the quality of today’s data deduplication tools and protocols, they remain a possibility and can cause serious headaches.

Added metadata

The deduplication process creates a new layer of metadata for change logs and the digital signatures attached to every processed block. This is called a “fingerprint file.” Not only does this metadata require storage space, but it may also create its own data integrity issues. If it becomes corrupted, for example, then the recovery process becomes significantly more challenging.

Cost of implementation

While data deduplication saves money in the long run via reduced space requirements, it does require an up-front investment. These costs include the dedupe tool itself, usually priced based on the number of records, as well as the IT staff time required to design, execute, and manage the deduplication process.

Data Deduplication Use Cases

How does data deduplication work in the real world? In theory, it’s a simple data science concept: Eliminate duplicate data to reduce resource consumption and minimize errors that happen when there are several versions of a file floating around. But different sectors, industries, and even departments have unique goals and needs. Here are some common use cases.

Customer relationship management: Within a CRM system, customer records, contact info, and deals may be recorded using multiple sources, levels of detail, and formats. This leads to inconsistent data, where one manager may have a slightly different record than another; for example, if the record for a point of contact is held in multiple data repositories and only one is updated after they leave the company, some employees will likely continue to use the outdated information. Data deduplication can help ensure a single source of accurate customer information, allowing every individual and group to use the latest data to generate visualizations or run analytics.

Data integration: When two organizations merge, whether through an acquisition or internal reshuffling, data contained in different instances of the same application can create duplicate records. Say a larger company purchases a smaller competitor with a 40% overlap in customers, and that’s reflected in their ERP systems. Deduplication can eliminate this redundancy, freeing up storage space while also ensuring that everyone within the newly formed organization uses only the latest version of each record.

Virtual computing: When using virtual desktops, such as for testing environments or virtual access for specialized applications or internal systems, data deduplication can increase efficiency—particularly with heavy user volume. Virtual machines often contain very similar data, which makes for many duplicate versions of files. Data deduplication purges these duplicates to help ensure storage doesn’t get overrun with data generated by virtual machines.

Banking: Within a financial institution, different departments or branches may hold duplicate records of customer information. Every duplicate record is a potential entry point for criminals to steal identities, make fraudulent transactions, and perform other unlawful activities. And examining and processing duplicate data to check for fraud requires more resources. Data deduplication can help improve efficiency and security for banks and credit unions.

This is just a sampling of use cases. Any organization that creates a lot of data can benefit from deduplication.

What to Consider When Choosing a Deduplication Technology

Numerous providers offer data deduplication tools, but which is right for your organization? Here are the key factors for teams to consider when making a short list.

• Performance: Different types of deduplication require different resources. For example, block-level deduplication that executes at the source on a large network will eat up significant resources compared with file-level deduplication executed at the target with a more limited scope.
• Scalability: Scalability and performance often go hand in hand because processes that chip away at performance are difficult to scale. This applies to deduplication, as the more resource intensive the process, the more difficult it is to scale up as needed. Organizations with wide-ranging scalability demands must consider these trade-offs when they choose a deduplication technology.
• Integration: Disconnected data sources can complicate the deduplication process. For example, when databases exist in silos, the probability of duplicate data is much higher. In other cases, a large network with multiple remote locations may require a more stringent cleansing and transformation protocol prior to deduplication. Organizations must assess the state of their data integration when considering how to implement deduplication.
• Cost: Deduplication tools vary in cost based on factors such as complexity and capability. Pricing increases based on the volume of records processed. Organizations should create a budget estimate based on industry standards and quoted rates, then assess how this is offset by long-term savings.

Eliminate the Need for Data Deduplication with Oracle HeatWave

The best way to resolve data deduplication problems is to minimize them in the first place. Oracle HeatWave helps with that by combining transactions, real-time analytics across data warehouses and data lakes, machine learning, and generative AI in one cloud service. HeatWave customers don’t need to duplicate data from a transactional database into a separate analytics database for analysis, which presents several benefits.

• There’s no need to store the same data in multiple data stores for different purposes.
• They don’t need complex, time-consuming, costly, and error-prone extract, transform, and load processes to move data between data stores.
• Analytics queries always access the most up-to-date data, which yields better outcomes versus analyzing data that can be stale by the time it’s available in a separate analytics database.
• There’s little risk of data being compromised in transit since data isn’t transferred between databases.
• HeatWave Lakehouse allows users to query as much as half a petabyte of data in the object store—and to optionally combine it with data in a MySQL database. Customers can query transactional data in MySQL databases, data in various formats in object storage, or a combination of both using standard MySQL commands, and without copying data from object storage to the MySQL Database.

With the built-in HeatWave AutoML, customers can build, train, and explain machine learning models within HeatWave, again without the need to duplicate data into a separate machine learning service.

HeatWave GenAI provides integrated, automated, and secure GenAI with in-database large language models (LLMs); an automated, in-database vector store; scale-out vector processing; and the ability to have contextual conversations in natural language—letting customers take advantage of GenAI without AI expertise and without moving data to a separate vector database.

By eliminating data duplication across several cloud services for transactions, analytics, machine learning, and GenAI, HeatWave enables customers to simplify their data infrastructures, make faster decisions that are more informed, increase productivity, improve security, and reduce costs. Additionally, customers get the best performance and price-performance for analytics workloads.

Data Deduplication FAQs

What is an example of deduplication?

An example of deduplication can come from running version-based backups and archives of an organization’s data. Each of these archives will contain many instances of the same untouched files. With deduplication, the backup process is streamlined by creating a new version of an archive without those duplicative files. Instead, the new version contains pointers to the single source, allowing it to exist within the archive without using up additional storage space.

What is the reason for deduplication?

Duplicate records needlessly eat up storage space. That additional storage space winds up taking more resources, including storage volume, transfer bandwidth, and compute resources, during processes such as malware scans. Deduplication reduces the volume of storage space used, shrinking overall resource use, be it bandwidth or storage capacity.

What is data duplicity?

Duplicates can emerge through both data duplicity and data redundancy. Data duplicity refers to situations when a user adds a duplicate file to the system themselves. Data redundancy refers to situations when databases with some overlapping files or records merge to create duplicates.

What are the disadvantages of deduplication?

Deduplication can free up storage space for greater long-term efficiency and cost savings. However, the actual process of deduplication is resource intensive and can slow down various parts of the network, including compute performance and transfer bandwidth. This means IT departments must think strategically about scheduling deduplication.