Data compression has existed for a couple of centuries already. Over the years, the format has changed to meet demands for more efficiency. We now create more digital data than we ever have before which is why it’s so important to effect data compression and minimize the cost of storing data and processing it. Here are five top tips for compressing big data to eventually reduce your costs, be more efficient, and gain better insights from the data.
1. File Formats
To make these files perform better, use either Avro or Parquet formats. The former is made up of binary format data and JSON format schema. This means you get maximum efficiency while reducing the file size. Avro is row-based, so it’s the go to for accessing each data set field. Parquet, on the other hand, are files made up of binary data with metadata attached. It’s column-based and is splittable as well as compressible. You can look at the column names, compression type and type of data without having to look through the file. Parquet allows for quicker data processing. This is better for accessing certain fields instead of all fields.
2. Compress Initially
The cost of big data mostly comes from the initial transfer to storage because it takes a lot of time and bandwidth to transfer a lot of files. It also takes a lot of storage to contain these files afterwards. The costs, bandwidth, storage, and your time can all be improved if the files are compressed to begin with, before you even transfer. This compression is a portion of the Extract, Transform, Load (ETL) process and it’s used when transferring data from a database. It extracts the data, changes it to the right use, and then loads it. This is automated for faster and easier process.
As per Frederic Jones, a tech writer at State Of Writing and Boom Essays, “if you have data management solutions, you can use built in features for this, like digital asset management tools. This also helps you change the file format so you only need to keep one version stored.”
You should try using co-processors to make your compression workflow more optimized. This helps you redirect your processing power and your time from the main CPU to a secondary unit. You can then keep your primary processor for analytics during compression time. To do this, use Field-Programmable Gate Arrays (FPGA). These are customizable microchips that can work as extra processors. Try to dedicate these FPGAs to compression only so your primary processors can focus on more important tasks. Queue your workloads so you don’t have to continuously monitor the progression.
4. Match Type
By changing the type of compression used you can really improve the process. These two sorts of compression are lossy and lossless. Lossy compression reduces the file size by eliminating certain data to get an approximation of the original data file. It’s mostly used for images, audio, and video since humans don’t notice certain missing data in media. Lossless compression is when repetitive data patterns are identified and assigned to a variable. Paul Doyle, a data blogger at Assignment Help and Research Papers, explains to his readers that “all data remains but the duplicated parts are removed. This is commonly used for text files, databases, and other discrete data.”
5. Data Deduplication
Data deduplication isn’t necessary for compression but it’s useful to reduce your data. It’s a way of comparing your stored data with new data and gets rid of duplicates. It’s not compression because it’s not reducing the amount of storage that you need to put the data. Instead, it eliminates whatever is deemed redundant and will point to the same file via a reference. Data compression is a great way to increase your efficiency and save your time by focusing on more complex tasks.
Aimee Laurence, a tutor with Finance Essay Help and Professional Essay Writers, shares her research on data, big data, and associated tools and software. She enjoys finding ways for people to use new technologies to optimize their data. Aimee also works in HR as a freelancer for Management Essay service.