Hot data is business-critical data that needs to be accessed frequently and resides on primary storage (NAS).
Hot data is considered to be of high value and importance. This type of data is typically stored in fast memory, such as RAM, to ensure quick and efficient access. Examples of hot data include frequently used databases, in-memory caches, and real-time data streams.
The term Hot Data is used in data management to refer to data that is frequently accessed or in high demand. The term Cold Data or Cold Data Storage refers to data that is infrequently accessed. The distinction between hot and cold data is essential for storage efficiency, especially when it comes to optimizing storage and retrieval processes in various systems as well as optimizing data storage costs. Hot data is usually stored in high-performance storage systems, such as solid-state drives (SSDs) or in-memory databases, to ensure quick access and response times. This is especially important for applications that require rapid access to frequently used information, such as transactional databases or real-time analytics.
In contrast, less frequently accessed or “cold” data may be stored on slower and more cost-effective storage solutions, such as traditional hard disk drives (HDDs) or archival systems. This tiered storage approach helps organizations balance performance requirements with cost considerations. Managing hot and cold data effectively is part of data lifecycle management, where data is classified based on its importance, access frequency, and other factors to optimize storage resources and overall system performance. Unstructured data management solutions like Komprise that are storage agnostic and do not get in the hot data path are increasingly popular for maximum data storage cost savings and ongoing data value.
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