How you process big data has huge upsides. If datasets are well managed, it’ll have a direct impact on the success of your business operations. Here we look at what cold and hot data storage involve, and differences to consider when deciding how best to process information.
What is cold data storage?
In a sense, cold data storage is information your business can’t live without. You need it for things like industry compliance, human resources or digital production. Cold data is more static in comparison to information that’s accessible daily. This means it needs to be on hand but not on the front line of your storage systems.
Why cold data matters
The main purpose of cold storage information is to retain it for future reference. How you store cold data differs from how hot data is retained. It doesn’t require swift access or to live on servers that can be transferred at speed. Instead, hard drives, internal servers with basic functionality and secure cloud storage are viable options for cold data. A popular solution to storing cold data is Hadoop. This is an open-source file distribution system that can be set up to automate the process of dormant files and datasets. This can be run on commodity hardware that is cheaper to maintain.
3 cold data storage upsides:
- Data optimization
- Economical storage
- Cost reduction
What is hot data storage?
While cold data is kept in low-performance, economical storage that takes longer to access, hot storage requires high-performance functionality and speed. Accessible team files need to be instantly available on the latest servers and easily transferred. Amazon (AWS) offers a tiered storage system with hot data operating on advanced cloud storage servers.
Why hot data matters
Hot data is active information that evolves and is used daily. That means it has to be agile and ready to be shared amongst colleagues. The removal of cold data from servers makes room for hot datasets to flow. It also declutters systems to improve efficiency.
Separation of hot and cold storage
Hot and cold data both have their virtues but are defined by their differences. The benefits that come from separation have a big impact on the success of business operations.
5 benefits of data separation:
- Improved efficiency
- Enhanced dataset quality
- Modified tiering systems
- Decluttered storage space
- Prioritized information
Data is constantly evolving and how you analyze and prioritize information plays a starring role in best practices. The ability to automate your cold data and prioritize hot data has a positive influence on future success. Consider what your business and teams need now and later, and store information accordingly.