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What Is Data Deduplication, and Why Should You Care?

The amount of data companies produce is constantly growing, and the growth rate isn’t slowing down. If anything, it’s accelerating. But what happens to all that data? Eventually, it piles up and fills their storage, slowing operations and incurring high costs. If companies don’t find an effective way to manage data growth and storage, these challenges are only going to get worse. Fortunately, data deduplication offers a refreshing solution to rampant data growth.

Data deduplication decreases storage capacity requirements by eliminating excessive copies of data from companies’ object stores. To accomplish this, deduplication software compares data to find duplicate information and only stores unique data. This process reduces storage costs and improves data management, making it a valuable tool for any business that wants to rein in its data storage.

Data deduplication is more critical now than ever before. Companies need their data in order to operate, but they also need to preserve available cloud storage. Deduplication lets them do both.

The importance of data deduplication

Data deduplication empowers companies to do more with their data. The practical advantages and benefits of this innovative technology include:

  • Storage Optimization: Storage space can be quite expensive and surprisingly limited. Eliminating duplicate data allows companies to maximize their storage space and reduce overall IT expenses.
  • Faster Backup and Restoration: Data deduplication reduces the amount of data companies need to back up and store. Quicker backup and restore processes enable companies to bounce back when their data is corrupted or compromised.
  • Bandwidth Optimization: Businesses can use data deduplication to reduce the amount of data they transfer across networks, thus optimizing their bandwidth usage. Companies with multiple offices and remote employees find this aspect of data deduplication especially useful.
  • Enhanced Compliance: Data deduplication strengthens data compliance by reducing the amount of unstructured data being stored and managed. Doing so can reduce the risk of data breaches and help companies stick to data protection regulations.

Types of data deduplication

There are two main types of data deduplication: inline and post-processing. While each has its unique benefits, many companies use a combination of the two to meet their data deduplication needs.

Inline deduplication analyzes data during the storage process. As the data is written, the system checks to see if it’s already present. If the data is duplicated, a pointer references the original data and removes data redundancies. Inline deduplication requires less backup storage, but it can increase latency.

Post-processing deduplication is an asynchronous backup process that removes redundant data after it’s written to storage. Duplicate data is extracted and replaced with a pointer to the first iteration of the block. This type of data deduplication allows users to dedupe specific workloads and quickly recover the most recent backup. However, a larger backup storage capacity is required compared to inline deduplication.

File-level vs. block-level deduplication

Data deduplication can occur at two levels: file and block. Both types provide advantages and disadvantages, and the best solution depends on the needs of the organization. Companies should consider their data deduplication needs, evaluate the two levels, and implement the one that checks all the boxes.

File-level data deduplication compares a file set to be backed up or archived with copies already stored. This comparison is made by checking the file’s attributes against the existing index. If the file is deemed unique, it is stored, and the index is updated. If the file is not unique, a pointer to the existing file is created and stored. Ultimately, only one instance of a file is saved. All subsequent copies are replaced with stubs that point to the original file.

Block-level deduplication examines a file and saves unique iterations of each block. Blocks are broken into chunks with a fixed length, and each chunk is processed using a hash algorithm. Block-level deduplication then creates a unique number for each piece and stores them in an index. So, if a file is updated, only the changed data is saved. File changes don’t create a separate, new file. This method is more efficient than file-level deduplication, but it takes more processing power and requires a larger index to keep track of the individual pieces.

Challenges of data deduplication

Data deduplication can be instrumental in helping companies optimize their storage costs and maximize their efficiency, but it doesn’t come without its challenges. To ensure the effectiveness of data deduplication, companies need to consider both the benefits and challenges.

  • Processing overhead: Deduplication can require considerable processing power, which directly impacts system performance. Overhead can increase when data deduplication requires additional processing power and resources to identify and compare data blocks to check for duplicates. The more data that’s deduplicated, the more processing overhead is required.
  • Increased complexity: Since deduplicated data is stored in a non-traditional format, it can be difficult to manage and manipulate. This can cause increased storage complexity. Additionally, metadata is required to track which data blocks are unique and which have been deduplicated. As deduplication increases, managing this metadata becomes even more challenging.
  • Data integrity: If not done correctly, data deduplication can compromise data integrity. First, while deduplication decreases the amount of redundant data and improves storage efficiency, the lack of redundancy can make it more difficult to recover data. Second, data deduplication can create an increased risk of data loss because if the metadata used to identify duplicated data is corrupted or lost, deduplicated data will become hard to recover. Lastly, deduplication can result in an increased risk of data corruption. If a corrupted data block is deduplicated, the corruption can spread to other deduplicated data blocks, causing widespread errors or data loss.
  • Deduplication ratio: With minimal duplicate data, the duplication ratio will be low. A lower deduplication ratio means less overall storage savings. However, increasing the deduplication ratio can improve storage efficiency, reduce backup times, and reduce network bandwidth requirements.
  • Limited Scalability: As we said, deduplication can require significant processing power to identify and compare data blocks. It can negatively impact the scalability of storage and backup infrastructure by slowing down processing times and increasing the risk of data loss. The deduplication ratio can also affect scalability because storage and network bandwidth requirements increase when the ratio is low.
  • Enhanced Compliance: Data deduplication strengthens data compliance by reducing the amount of unstructured data being stored and managed. Doing so can reduce the risk of data breaches and help companies stick to data protection regulations.

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Panzura helps companies deduplicate their data

Panzura CloudFS is a global file system that stops file-level duplication before data is synced to the object store. The file system will store only unique copies of files, so data is deduplicated before it’s even stored. Additionally, Panzura performs inline block-level deduplication on data in the object store. This approach removes duplicate blocks across different files.

Panzura stands out from other deduplication providers because it embeds the deduplication reference table in metadata that is instantly shared among all Panzura nodes. Inline deduplication removes data redundancy across all nodes, allowing each node to benefit from data seen by all other nodes. This process provides better capacity reduction and guarantees that all data in the cloud is unique, thus lowering the cloud storage and network capacity required.

Global deduplication enables CloudFS to deduplicate redundant data before it’s moved to a company’s chosen object store. Rather than examining complete files, Panzura examines the individual blocks that comprise a file and deduplicates them at the block level. While files in their entirety may not appear identical, there may be duplicate blocks of data within those files. CloudFS enables the deduplication of those identical elements.

Companies that utilize Panzura’s CloudFS for data deduplication will experience a significant decrease in their data footprints. Not only does CloudFS enable this deduplication, but it also maintains it at all times by checking for redundancies every time data is moved into cloud storage.

Cloud storage is a vital resource for modern companies, and they shouldn’t have to struggle to use that resource to its full potential. We believe that companies deserve to keep all their data without living in fear of running out of storage. That’s why Panzura stays ready to help organizations of all types and sizes maximize their cloud storage through deduplication.