Data deduplication vs. data compression

Big data is taking over enterprises, businesses, and entire industries left and right in our rapidly advancing technology-filled world. If there’s ever been a time when working with vast quantities of data was an increasingly sought-after skill, that time would very likely be now.

Data deduplication and data compression are two important methods used by data scientists, analysts, and enterprises to reduce storage capacity for data backups. While data compression has been around for many years and is a proven and effective method for “shrinking” down large quantities of data, deduplication is a more specialized and potentially more effective technique that results in more savings and reduced file sizes.

Managing large data sets has never been easy, and understanding both deduplication and compression at a deeper level is essential if you want your business to stand out and obtain the necessary skills to outsmart and outperform the competition. We’re going to discuss both processes in detail, dive into how they work, look at when you should use each process, and cover several common use cases for data deduplication.

What is data compression?

Data compression reduces the length or size of information and data by removing unnecessary fillers and spaces while maintaining the same meaning of the information being compressed. Sophisticated data compression algorithms such as LZ77, LZR, Deflate, LZ, LZMA, and many more are used to compress files and stored data into the smallest possible amount of stored bits.

Different algorithms will be more efficient and effective depending on the compression requirements, speed considerations, and volumes of data being compressed. Additionally, considerations as to whether or not any data could be lost during compression need to be taken into account.

Methods of compression

Depending on the compressed data type, either a lossy or a lossless compression algorithm can be used. We’re going to explore the differences between both and how they can impact the underlying compressed data.

• Lossy Compression – In a lossy compression algorithm, bits and pieces of the data being compressed are lost during the compression process. While this might sound scary at first, it’s perfectly fine in certain situations, such as the compression of image files. Images consist of thousands of pixels, and an algorithm that loses or slightly distorts some pixel data would still be completely indistinguishable from the original image in most cases.
• Lossless Compression – When lossless compression algorithms are used to compress data, there is no underlying data loss. This means that after a lossless compression algorithm is run, the compressed contents can be retrieved and transformed into a pristine copy of the original. This is required for sensitive data and areas where precision is required, such as when even a single bit can alter the meaning of the data altogether.

Lossless compression is often necessary and preferred when working with e-commerce data, websites, and databases.  When customer data is being compressed in order to save server space, for instance, that data must be able to be retrieved as an exact copy without any data loss throughout the compression process.

Unfortunately, most lossless compression methods today don’t have good compression ratios for large data sets, requiring alternative methods to be used.

What is data deduplication?

Compared to data compression, data deduplication is a superior method of lossless compression for a wide range of data types. The principle of data deduplication can be found in its name, where duplicate parameters or bits of data are replaced with a hash number or a pointer, significantly reducing the size of the data being processed.

How does data deduplication work?

When vast quantities of data come in from multiple sources, common symbols, characters, or parameters can be present in each data set. Deduplication takes these redundant and repeated parameters and replaces them with a single identifier or pointer while at the same time saving only one copy of the data containing the hash or pointer towards a single copy.

In other words, deduplication is the process of identifying repeated patterns and then seamlessly moving those patterns or chunks, along with special pointers to identify the repeated patterns, into a unique copy of the deduplicated data.

No data is lost throughout the deduplication process, and it is a completely “lossless” method for reducing the size of large data quantities.

When to use data deduplication

Whether or not you should use data deduplication in your specific use case depends on several factors, the most important being lossless data compression requirements. Enterprise e-commerce businesses that need to compress data logs, access logs, purchase records, or customer information need a lossless method like deduplication to keep their data safe. Photographers, on the other hand, would be fine using lossy compression since small changes to pixels in a photo are undetectable to the human eye.

Advantages of deduplication

Deduplication has many advantages, such as reduced data storage costs, better customer experiences, improved analytics and segmentation, and the ability to consolidate redundant data. We’ll walk through each of these advantages individually and examine how certain data sets can benefit more than others.

• Reduced cost of data storage – Cloud storage is incredibly popular these days, but it comes at a cost. Every single bit of data stored on cloud servers is often charged by the minute, and any size reductions can significantly impact expenses and costs. Deduplication results in smaller file sizes, which in turn reduces data storage and hosting costs.
• Consolidating redundant data – Deduplication consolidates redundant data and replaces it with pointers or hashes. If you’re working with a data set that has a large number of repeating patterns, you’ll see the biggest file size improvements from deduplication.
• Improved segmentation and analytics – Implementing deduplication into your company’s data sets can result in improvements to your data analytics capabilities and segmentation. When your data is optimized and stored more effectively, there’s more room build highly targeted audiences and grow your bottom line.
• Better customer experience – Deduplication will likely result in improved customer experiences to anyone working with your business due to impressive file size reductions made possible through the deduplication process. You’ll be able to store more customer data, and therefore, provide more customization and personalization options to your most loyal buyers.

Disadvantages of deduplication

Even though deduplication has numerous advantages, there are also several disadvantages everyone looking to use deduplication should be aware of. Some of the most significant disadvantages of this process are slowed performance speeds and the potential for data integrity losses from improper matching.

• Slowed performance speeds – Deduplication can result in slower performance speeds during the deduplication process, taking up more time compared to alternative algorithms and compression methods.
• Loss of data integrity from improper matching – If data is improperly matched during the deduplication process, it won’t be able to be losslessly retrieved. This can result in lost data during the deduplication “decoding” process.
• Not efficient where lossy compression is OK – When lossy compression works just fine, such as with online image compression, deduplication would be an inefficient method to utilize.

Data deduplication use cases

Enterprises invest in data deduplication software when they expect to use cloud storage backups for their data, have big data marketing requirements, or need to be able to work with scalable identity resolution.

Cloud storage backup

Cloud storage backups can be a significant expense for enterprises that have vast amounts of data stored in the cloud. With deduplication, costs can be reduced significantly by lowering the file size of the data being stored.

Scalable identity resolution

When performing entity or identity resolution across large data sets, the ability to compress each data set for storage and access each data set on demand is critical. Entity resolution processes can be simplified, sped up, and improved using deduplication.

Big data marketing

Enterprises that run marketing campaigns that collect vast amounts of data could benefit significantly from deduplication. Big data marketing requires all of the collected data to be archived and stored, making it the perfect candidate for deduplication to reduce file and data sizes in a lossless manner.