Understanding Deduplication: Improving Efficiency with Data Hygiene

Key takeaways

Deduplication is a data management strategy that removes duplicate data points to improve storage efficiency and program performance.

• Two common types of deduplication include inline and post-process, with the optimal approach depending on your data structures and available resources.

• Data deduplication is important because it lowers your data storage costs and simplifies disaster recovery.

• You can use tools, such as data management software, storage appliances, and cloud-based solutions, to automate deduplication processes. 

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What is deduplication?

Deduplication is a type of data management focused on finding and removing duplicate data. This process keeps only unique instances, even when multiple files or data sets share blocks of data. This saves storage space on your device, improves the efficiency of your programs, and can reduce overall costs. For example, if several employees in an organization store the same email attachment on a shared server, deduplication models consolidate the same data into one instance instead of taking up storage space with redundant files.

By storing only unique information, you can better manage large data sets without wasting resources or raising costs. This is important across industries that use data to drive decision-making, including finance, health care, and information technology (IT). 

Deduplication software tools and technologies

You can choose between many types of tools and technologies to help automate and streamline the deduplication process. Three types of tools to consider include data management software, storage appliances, and cloud-based solutions. Consider a few examples below:

• Data management software: Veritas NetBackup, Commvault

• Storage appliances: Dell PowerProtect Data Domain, HPE StoreOnce Systems

• Cloud-based solutions: Amazon S3, Microsoft Azure File Sync

Types of deduplication

You can opt for either inline deduplication or post-process deduplication, depending on your organization’s data structures and resources available. 

Inline deduplication 

Inline deduplication occurs in real time. If your company wants to limit bandwidth requirements, this is a great choice because duplicate data is never transferred or stored; it is processed and removed as the data enters the pipeline.

Post-process deduplication

Post-process deduplication occurs after you’ve entered and stored the data. You can complete the deduplication process at any time after data entry and storage, and it allows you to deduplicate specific workloads or recover recent backups. If you are concerned about the computational power associated with real-time inline deduplication, you might choose this option.

Methods for deduplication

Several deduplication methods are available depending on your organization’s needs and resources. Each method approaches data differently, so it’s important to find the one that aligns with your data environment.

File-level deduplication

File-level deduplication compares entire files and removes duplicate copies. If your organization has many copies of identical files, such as backup archives, this can be an effective method of reducing data storage usage. 

Block deduplication

Block deduplication, or sub-file deduplication, is the most prevalent type of data deduplication. It operates by identifying repeated blocks of data and removing them. This method is more flexible than file-level deduplication because it compares sections of files rather than the entire file itself. 

Byte-level deduplication

The most granular form of deduplication, byte-level deduplication, can understand the content of data and deduplicate specific bytes within the data stream. This method has the biggest storage-saving effect because it can recognize data blocks with identical byte patterns, which is especially beneficial for deduplication in environments with minor file changes or highly variable data.

What is an example of data deduplication?

An example of data deduplication is customer relationship management (CRM) systems, where several data points are recorded for customers, often from multiple sources, which can lead to duplications and inconsistencies. Implementing deduplication helps ensure customer information is accurate and up to date.

Why data deduplication is important

Data deduplication not only reduces the computational load on storage systems but can have far-reaching benefits across organizational infrastructure. When deciding whether to prioritize data deduplication, consider the following benefits:

Lowering overall costs

Storage space costs money, and costs often increase significantly as space requirements increase. Decreasing your organization's storage needs can reduce expenses and allow you to direct resources to other types of organizational operations. 

Using less bandwidth 

When you don’t need to transfer as much data to remote storage locations, you require less bandwidth for data management. Inline deduplication is particularly effective for this. 

Improving data backup and recovery efficiency

By reducing the amount of data your organization needs to process, you can more efficiently back up and recover your data. This is especially valuable for disaster recovery efforts, as having effective deduplication and data management procedures can help to minimize data losses.

Challenges of deduplication

Overall, challenges for deduplication center on heavy resource use and the risk of data loss. Because you are only storing one instance of the data, if this version becomes corrupted, you may lose information without a backup. Since deduplication can be resource-intensive, you will need to closely monitor system performance to ensure adequate bandwidth and timely data processing. 

In addition, several methods of deduplication may have their own challenges or be unsuitable for specific data types. For example, if you have data stored in alternate formats, such as images or email repositories, file-level deduplication may be unable to detect duplicates, making it ineffective for this type of application. Unstructured data and changes at the sub-file level aren’t compatible with this type of deduplication, so it’s important to understand your data structures before choosing this method.

How to choose the right deduplication method 

To determine the right deduplication method, you’ll need to examine several internal variables that affect how your organization creates, stores, and processes data. Questions to consider before selecting a method include:

• How many types of data sets do you have?

• What type of data are you storing?

• How much duplicate data do you have?

• Which storage system are you using?

• What type of virtual environment are you using?

• What types of applications does your company use?

By carefully considering these questions, you can decide whether inline or post-process deduplication is right for you and whether to opt for file-level, block, or byte-level deduplication algorithms.

Read more: What Is Big Data Storage? Definition, Uses, and More

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