A data compression algorithm codes information to reduce the amount of required storage to save the data and decodes it when users request it. While every algorithm works using its own strategy, businesses use data compression to save money on storage space. Compression is also common with data transfers for performance and cost savings on bandwidth.
What Are Data Compression Algorithms?
Data compression algorithms take a file and code it into a compressed state. For example, you could compress a gigabyte file into a few megabytes. Algorithms use a codec to rewrite the way a file is stored, and a decoder reassembles the file into its original state.
Every codec must compress a file in a way that avoids data loss. Good data compression algorithms have data “lossless” features. Compression that leads to corruption of decoded data or a complete loss of information affects data integrity. The algorithm you use should be tested and verified before using it on critical business applications.
Types of Data Compression Algorithms
There are several different compression algorithms and applications, some more popular than others. For example, GZIP is common with Linux users, and it works with the DEFLATE compression algorithm. WinZip, which was popular many years ago, incorporates ZIP compression using a dictionary-based algorithm.
GZIP and ZIP focus on standard text documents. Some compression algorithms target sound, images, or video files. The JPEG compression algorithm is commonly used for images to reduce the amount of storage space necessary for pictures. MP3 compression works well with audio files. MPEG and WMV are commonly used compression algorithms for video files.
How Data Compression Algorithms Work
Data compression works by taking original bits and storing them—called encoding—with a smaller number of bits. The stored bits have a pattern that enables compression algorithms to rebuild the file—called decoding. Encoding and decoding files must be fast, or the compression algorithm is useless in a high-performance environment.
Encoded data stays on a storage device until it’s retrieved. When a user retrieves a file, a decoder reassembles the file to its original state and loads it into memory. When a user changes the file, the encoder compresses data again and stores it into its newly encoded state. For example, a 100MB file could be encoded to 50MB, so the compression rate is 50%.
Benefits of Using Data Compression Algorithms
Reducing the size of a file speeds up data transfers and lowers the cost of storage by lowering storage requirements. For companies paying for limited bandwidth, compressing data files before sending them to a recipient reduces internet service provider (ISP) costs. Compressing files before storing them also reduces the amount of disk space needed for storage, which helps lower storage costs.
Whether you work with a cloud provider or store files on premises, data compression saves on infrastructure costs. When you have terabytes of data and archives of that data, storage costs can get expensive. Compressing files frees up a large percentage of this storage space.
Applications of Data Compression Algorithms
Data compression is often used with large files, so applications working with audio or video often use at least one data compression algorithm. Companies under strict compliance regulations with data retention requirements might need to keep archives of old files. Compressing these files saves on storage space, which helps lower infrastructure costs.
Streaming media requires large bandwidth allowances, but compression algorithms reduce the size of a file before sending it to a recipient. The bandwidth you have can store larger amounts of file data, so users receive their files faster. The file can then be decompressed when it reaches the recipient.
Conclusion
If you have a lot of files that you need to archive, transfer, or store for long periods of time, data compression can help you save on storage and bandwidth costs. You can choose the compression algorithm that fits the application, but many applications come with their own built-in compression. Compressing files for business storage can reduce large amounts of disk space and reduce your current costs for infrastructure.
To help with your storage requirements, Pure Storage® Purity and FlashArray™ can work with your preferred compression algorithm and business strategy.
