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Warm data is a term for data that gets analyzed on a fairly frequent basis, but is not constantly in play or in motion. By contrast, hot data is data that is used very frequently and data that administrators perceive to be always changing. Handling requirements for warm data can be less stringent than those for hot data, because of the lower amount of activity that takes place around warm data sets.
Scrutinizing the frequency of data use is important to companies for various reasons. With hot data, there is a lot of worry about data resolution – whether some piece of data in one part of the system is going to match its corresponding piece in another part of the system. There are also a multitude of other concerns such as encryption for security, latency and access, and transparency.
For warm data, resolution is not as much of an issue, because the data is not constantly looked at. There may still be security and access issues – and there is still the responsibility to provide a mechanism for retrieval. Warm data has to be archived, but because it can be dormant for a while, the archiving process can be slightly less complex. Take the example of a hotel that uses a complex digital booking system. The individual customer records are often considered hot data – they are always changing, in tabled fields such as customer identifier, night of stay, amenities, etc. These may have to be synchronized in middleware and a central data center. By contrast, group accounts might be considered warm data – they may sit for a while without being used, so they may not need the same level of attention. The ways that companies treat warm and hot data say a lot about their digital strategies,especially as firms move to public, private and hybrid cloud systems and assess workload requirements.