Elastic data tiering strategy: Optimizing for a resilient and efficient implementation

Organizations often start using Elastic for specific use cases, but expand its adoption for various purposes due to its flexibility. Storing data is insufficient in modern IT environments; organizations need solutions that enable quick and effective data utilization. Optimizing data storage across tiers can lead to cost savings and enhanced data value. A recent architecture review revealed that most high-volume data was searched within 24 hours of ingestion, while post-24-hour usage mainly involved ad-hoc security investigations and long-term reporting. Implementing a hot/cold/frozen architecture addressed these needs by migrating data from the hot tier to the frozen tier after 36 hours, keeping necessary data in the cold tier, and expanding the frozen tier for increased search performance and longer data retention. Storage optimization techniques such as searchable snapshots and reduced data replication further improved storage density and reduced hardware requirements. The new architecture aims to consolidate hardware profiles for logging and security workloads, optimize storage, and enhance data retention and platform management, leading to a better return on investment. When planning capacity for each tier, organizations should consider individual storage and performance requirements and ensure balanced and efficient resource allocation.