Big Data Compression- How Businesses Can Make It Simpler
Over the years, Big Data and Analytics have emerged as a key business technology. Today, organizations rely greatly on information for insights that have the potential to drive smarter decisions. Businesses generate data every day and wouldn’t want to miss out on the insights it can provide. While this technology serves immense benefits, handling such a large volume and variety of information is often a challenge. Organizations require adequate processing power and efficient storage to store, manage, and use it effectively.
Fortunately, big data compression helps in addressing these demands by cutting down the amount of bandwidth and storage needed for handling such massive data sets. Additionally, compression can eliminate redundant and irrelevant pieces from your systems, which makes processing and analysis easier and faster. There are cost-cutting benefits as well. However, you need to do things right to unlock these benefits, but the sheer volume of corporate data makes it tough. Here are some tips that businesses can rely on for simplifying big data compression.
Leverage a co-processor
Since you will probably have to handle a massive volume of information, the best thing to do would be to leverage a co-processor for optimizing your compression workflow. The approach cuts down the burden on your main CPU by redirecting the time and processing power to secondary ones. You end up retaining the primary processor for data processing and analytics while compressing it simultaneously with the help of co-processors.
For this purpose, you will have to configure Field Programmable Gate Arrays (FPGAs) to serve as co-processors. Dedicating FPGAs to compression enables frees up the primary processes so that they can focus on more critical tasks. Further, you can compress several data sets by using them, even with minimal monitoring.
Compress from the start
The initial transfer of Big Data into storage translates into a significant cost for a business. It often takes a lot of time and bandwidth to transfer a large number of files. Further, the storage requirements are also high for such voluminous data. You can cut down on time, bandwidth, and storage by just compressing the files before or during transfer, which is certainly a smart approach.
It can be done using the Extract, Transform, Load (ETL) process. As the name suggests, ETL is used for extracting data, transforming it to make it usable in the target system, and loading the transformed data. Since these tasks are carried out with automated pipelines, the process is fast and easy.
Choose the file formats wisely
Another key factor to bear in mind while compressing Big Data is file formats. Essentially, you need to understand the difference between lossless or lossy options. The lossless method, as the name implies, preserves all data and ensures its retrieval on decompression. RAR is a popular instance of the lossless method. It is a simple format that lets you compress files without losing quality. Also, you can easily unpack any rar files on Mac or other OS. So data retrieval is not a concern with this format.
Conversely, lossy compression creates only a rough approximation of the original data and eliminates it as well. Lossless compression is ideal for text documents, databases, and discrete data, while the lossy method works for images, audio, and video. Consider using an optimal mix of both, depending on the file types.
Select codec carefully
Codec is the acronym for compressor/decompressor and refers to software, hardware or a combination of both. Codecs are used for applying compression/decompression algorithms to data, so they play a vital role in the process. The data and file type to be compressed determines the type of codec you can use. The choice also depends on whether you want the file to be splittable.
Snappy, gzip, LZ4 and Zstd are some popular codecs used for Big Data compression. Snappy is commonly used for database compression, while gzip enables HTTP compression. LZ4 works for general-purpose analysis, and Zstd is designed for real-time compression. Consider the specific needs for data sets and choose a codec that works for them.
Optimize JSON performance
Avro, a row-based format, is compressible and splittable and reduces the file size to enhance efficiency. Parquet, on the other hand, is a column-based format that is also compressible and splittable. The format enables tools such as Spark to find column names, data types, and encodings without even parsing the file, which also speeds up the process.
Combine with deduplication
While deduplication is optional, it brings efficiency for Big Data compression by further reducing the data volume to be handled. The process compares the data you want to store with the current one and eliminates potential duplicates. It makes sense to steer clear of duplicates as they will only exert pressure on the storage and bandwidth without serving any real benefits to the business.
Deduplication uses references to highlight a single file that replicates duplicate information so that you can utilize it in multiple data sets. The process is quite similar to the one used by lossless compression algorithms. Deduplication can be used for whole files as well as on a block level, depending on your requirements. It goes a long way in simplifying the process and reducing the clutter for the entire system.
Big Data is valuable for businesses because it offers the information you need for making more insightful decisions. However, the benefits it offers are bundled with the complexities and costs of storage and processing such large volumes of data. Moreover, there is always a risk of redundancy because it is hard to unearth and eliminate duplicates amid such massive volumes. Fortunately, a smarter approach to compression can address these challenges and help your business make the most out of the data gathered over time.