There are a number of different data deduplication processes that influence the way data deduplication works. In essence, data deduplication functions by creating and comparing groups of data called “chunks.” However, there are several variables that dictate how each of these different data deduplication processes work.
You can either run inline deduplication or post-processing deduplication. The difference between the two is that with post-processing deduplication, duplicates are removed after the data has already been written on a disk. With an inline process, on the other hand, deduplication is run as the data is being written into the storage system. With data deduplication software, you can run both post-processing and inline data deduplication to maximize savings.
No matter which you use, the basic steps of deduplication operate the same way. In order for data to be deduplicated, it is first broken down into chunks. These are typically one or more contiguous blocks of data. Every deduplication system creates chunks differently, but no matter which way the chunks are broken down, the process of comparing the chunks is largely the same.
Once the data is broken down, the analysis process begins. Each individual chunk is run through an algorithm that creates a hash—essentially a long series of numbers and letters that represent the data contained in the chunk. Given that even the smallest change to the data in a chunk causes the hash to change, two different chunks that result in matching hashes are considered identical. Whenever a chunk is found to be redundant, it is replaced by a small reference pointing to the stored chunk.
Which Data Deduplication Method Is Right for You?
A further distinction between data deduplication methods is between target and source deduplication. The basic distinction between the two is that target deduplication occurs near the location where the data is stored, whereas source deduplication occurs near where the data is created.
In target deduplication, the process of removing duplicates occurs when the data reaches the target storage device. Once the data actually reaches the target, deduplication can either be done before or after the data is backed up to the device. That means the server is unaware of any deduplication efforts because the chunking and comparison work occurs at the target. This is generally the more popular method, though it does have some disadvantages compared to source deduplication.
In source deduplication, the process of removing redundant data occurs at the source instead of at the target. It typically takes place within the file system itself, where periodic scans of new files occur. The resulting hashes are then sent to the backup server for comparison. If the server finds the chunk to be unique, it is then transferred to the backup server and written to the disk. But if the server finds any identical hashes already in the system, then the chunk is not unique and does not get transferred to the backup server. This saves both storage and bandwidth.
One common criticism of source deduplication is it uses a lot of CPU power—more than target deduplication. However, given the significant reduction in the amount of CPU needed to transfer backups, the increased amount of CPU used in the source deduplication process is typically offset in the long run.
The main difference that needs to be considered when determining the right data deduplication method for you is in how the deduplication processes actually play out. With the target deduplication method, you need to buy target deduplication disk appliances. These appliances need to be present everywhere you’re going to back up. While this can be costly, it offers the additional benefit of allowing for incremental deduplication. With incremental deduplication, you use the same backup software, but simply change the target. It also lets you conduct target deduplication with almost any backup software, as long as it is one that the appliance supports. That means that you don’t need to embark on a wholesale replacement of your entire backup system.
With source data deduplication, you typically do need to undergo a wholesale replacement of your entire backup system. However, unlike target deduplication, you don’t need an appliance that’s local to each device you want to back up. Since you can back up from anywhere with source deduplication, it is the ideal data deduplication method if you have a lot of remote devices like laptops and mobile devices.
Data Deduplication in the Cloud
The increased use of the cloud is opening up amazing possibilities for data deduplication. Some of the best data deduplication ratios can often be achieved through virtual server environments. This is because when it comes to virtual environments there is a huge amount of redundant data that can easily be removed through a data deduplication process.
With more and more companies moving to virtual cloud environments for their data storage, data deduplication is also opening the door for new possibilities with stored data. In particular, it is improving data governance. By providing historical context for information, data deduplication is improving IT’s ability to understand data usage patterns. This understanding can then be used to proactively optimize data redundancies across users in distributed environments.
What Is a Deduplication Ratio?
As previously mentioned, a data deduplication ratio is the comparison between the original size of the data and its size after the redundancy is removed. It is essentially a measure of the effectiveness of the deduplication process. As the deduplication ratio increases, the deduplication process returns comparatively weaker results, given that most of the redundancy has already been removed. For example, a 500:1 deduplication ratio is not significantly better than a 100:1 ratio—in the former case 99.8% of data is eliminated, versus 99% of data eliminated in the latter.
The factors that have the greatest influence on the deduplication ratio are:
- Data retention. The longer that data has been retained, the greater the likelihood of finding redundancy.
- Data type. Certain types of files are more likely to have high levels of redundancy than others.
- Change rate. If your data changes frequently, you will likely have a lower deduplication ratio.
- Location. The wider the scope of your data deduplication efforts, the greater the likelihood of finding duplicates. For example, global deduplication across multiple systems typically yields a higher ratio than local deduplication looking at a single device.
Why Is Data Deduplication Important?
Data deduplication is important because it significantly reduces your storage space needs, saving you money and reducing how much bandwidth is wasted on transferring data to/from remote storage locations. In some cases, data deduplication can reduce storage requirements by up to 95%, though factors like the type of data you are attempting to deduplicate will impact your specific deduplication ratio. Even if your storage requirements are reduced by less than 95%, data deduplication can still result in huge savings and significant increases in your bandwidth availability.
There is no single right way to engage in data deduplication. Luckily, there are many different variables that can help you find the best approach for your environment. From inline to post-processing to target to source deduplication, there are a variety of approaches that can all result in significant decreases in your storage capacity needs. This, in turn, results in significant cost savings for your organization.
For more information on data deduplication read through our related blog articles.