What is Data Deduplication? A Plain-English Guide

Data Deduplication: The Ultimate Guide

As data continues to grow at an exponential rate, organizations are turning to data deduplication to help manage ballooning storage requirements and costs. In fact, IDC predicts that the amount of data created and replicated will reach 175 zettabytes by 2025. For enterprise data centers already struggling to keep up, deduplication offers a way to dramatically reduce the amount of data that needs to be stored.

In this ultimate guide, we‘ll dive deep into data deduplication—what it is, how it works, the benefits, use cases, what to look for in a solution, and more. By the end, you‘ll have a comprehensive understanding of this critical data reduction technology and how it can help your organization.

What is Data Deduplication?
In simple terms, data deduplication is a technique for eliminating redundant data to improve storage utilization. It works by identifying duplicate data patterns and replacing subsequent copies with a reference to the original. Only one unique instance of the data is actually retained.

Data deduplication can be performed at the file, block, or even the bit level:

File-level deduplication eliminates duplicate files. If the same file is stored in multiple locations, file-level deduplication will keep just one copy and replace the others with a pointer to the original.

Block-level deduplication looks within a file and saves unique iterations of each block. For example, if you save a new version of a document, only the changed blocks are saved; the rest is replaced with pointers to previously stored blocks.

Bit-level deduplication, also called byte-level or variable-block, breaks files into chunks of varying sizes and looks for duplicates at an even finer-grained level.

The type of deduplication you use depends on the specific applications and data types. File-level works well for unstructured data while block and variable-block are better for structured data like databases.

How Data Deduplication Works
While the specifics may vary depending on the vendor and product, all data deduplication systems rely on the same basic workflow:

  1. Divide the input data into chunks or blocks. The block size can be fixed or variable depending on the solution.

  2. Calculate a hash value, such as SHA-1 or MD5, for each block. The hash acts as a unique fingerprint identifying the data block.

  3. Use the hash to check if the same data block has been previously stored. If it has, replace the duplicate block with a reference to the original.

  4. Store the hash and block mappings in an index for easy lookup and retrieval.

  5. If a block doesn‘t already exist, compress it and store the compressed block.

The deduplication process can be implemented a few different ways:

Inline vs post-process: With inline deduplication, data is deduplicated in real-time as it‘s ingested. In contrast, post-process deduplication writes the data first and then deduplicates it later according to a schedule. Inline is more space-efficient but can impact write performance. Post-process consumes more space but has no impact on data ingestion.

Source vs target: Source-based deduplication happens close to where data is created while target deduplication takes place where the data is stored, such as a backup appliance or storage array. Source deduplication reduces the amount of data that needs to be transferred over the network but distributes the processing load. Target deduplication centralizes the dedupe function but requires moving more data.

Benefits of Data Deduplication
So why go through all this trouble to deduplicate data? It turns out there are some significant benefits:

Storage savings: The most obvious benefit of deduplication is the ability to store more data in the same amount of space, or retain data for longer periods of time. Deduplication ratios can range anywhere from 2:1 for general file servers to as high 95% for full backups with only incremental changes. It‘s not uncommon to see overall data reduction rates of 10:1 to 30:1 or more.

Reduced costs: Storing less data translates directly into cost savings, not only on the raw cost of storage but also in the data center space, power and cooling, and administration required to manage it.

Increased efficiency: Redundant data slows everything down—backups and recovery, replication, data migration. Deduplication helps reduce network traffic and enables more efficient use of resources.

Improved reliability: Reducing the overall data footprint mitigates issues like "backup window creep" where nightly backup jobs extend into the next workday. This improves backup success rates and recovery times.

Common Deduplication Use Cases
Data deduplication is used in a variety of scenarios to help organizations cope with massive data growth:

Backup and recovery: Eliminating redundant data helps speed up backups and reduces the time and cost of storing backup files. Virtual machine backups in particular often have only small incremental changes that are well-suited for deduplication.

Disaster recovery (DR): Deduplication can dramatically reduce the amount of data that needs to be replicated to a secondary DR site. This enables more cost-effective DR solutions and faster recovery times.

Data migration: Deduplication makes data migrations faster, less expensive and more efficient by shrinking the amount of data to be transferred.

Test/Dev environments: Redundant data copies are inherent in test/dev setups where the same data is used repeatedly. Deduping this data frees up tons of space.

What to Look for in a Deduplication Solution
When evaluating data deduplication products, there are a number of factors to consider:

Deduplication method: Choose between file, block, or variable block level deduplication depending on your specific data types and use case. Many solutions support multiple methods.

Inline vs post-process: Consider the tradeoffs between inline and post-process in terms of performance, space efficiency, cost, scalability, etc. Some products offer a choice of inline or post-process.

Source or target: Source deduplication reduces network traffic but adds overhead on the data source. Target dedupe offloads processing but requires more data to be transferred. The right deployment model depends on your environment.

Scalability and performance: Look for a solution that can scale seamlessly to handle high data volumes and velocities without impacting application performance. Consider features like load balancing, parallel processing, and high availability.

Security: Make sure the deduplication system supports encryption of data both in-flight and at-rest. Role-based access controls, multi-factor authentication, and audit logging are also important for preventing unauthorized access.

Cloud readiness: Even if you‘re not using cloud storage now, you likely will in the future. Consider a deduplication solution that supports leading public cloud platforms like AWS, Azure, and Google Cloud.

Some of the top data deduplication solutions on the market include:

  • Dell EMC PowerProtect DD (Data Domain)
  • HPE StoreOnce
  • Veritas NetBackup
  • IBM ProtecTIER
  • Cohesity DataPlatform
  • ExaGrid EX Series
  • Quantum DXi-Series

Potential Challenges with Deduplication
While data deduplication offers many benefits, there are some potential drawbacks and challenges to be aware of:

Performance impact: The data deduplication process consumes CPU, memory and I/O resources which can impact application performance, particularly with inline deduplication. Post-process deduplication minimizes this impact but takes up more space.

Scalability limits: Hash collisions (where two different blocks hash to the same value) become more likely as the amount of data scales. This can result in data corruption if not properly handled. Some products have limits on the size of the deduplication repository that may require forklift upgrades.

Increased fragmentation: Over time, the deduplicated data store can become fragmented which degrades read performance. Periodic rehydration may be required to restore data locality.

Vendor lock-in: Deduplication algorithms are often proprietary which can make it difficult to move data between different deduplication systems. Consider products that support open standards where possible.

The Future of Deduplication
As data volumes continue to grow, data deduplication will only become more critical. Newer technologies such as flash storage and non-volatile memory express (NVMe) are driving changes in how deduplication is implemented.

For example, inline deduplication can take advantage of the parallelism in NVMe to minimize the performance impact. By deduplicating data before it‘s written to flash, these systems can extend the life of SSDs and reduce write amplification.

Artificial intelligence and machine learning techniques are also being applied to data deduplication to improve efficiency and precision. By training models to identify duplicate data patterns, these intelligent systems can make deduplication faster and more accurate over time.

Looking ahead, data deduplication will coexist with other data reduction technologies like compression, erasure coding, and delta differencing in the race to control data growth. The ultimate goal is not only to reduce storage consumption but to place the right data in the right place at the right time—wherever that may be.

Data deduplication is a powerful tool in the storage admin‘s toolbox to help tame data sprawl. By eliminating redundant data at the file, block, or variable level, deduplication enables organizations to store more data at lower cost and transmit data more efficiently over networks.

When selecting a deduplication solution, it‘s important to match the methods and deployment models to your specific data profile and use cases. Consider scalability, performance, security and cloud readiness.

With data only continuing to grow, it‘s not a question of if but when you‘ll need deduplication in your environment. Understanding how this technology works will help you take full advantage of its many benefits.