If you’re using Elasticsearch as part of an ELK stack solution for log analytics, you’ll need to manage the size of your indexed log data to achieve the best performance. Data retention policies establish how long your log data will be retained in the index before it is automatically expired and deleted, which can impact both storage costs and analytics value. Shorter data retention windows result in lower storage costs but limit access to older log data that could support long-term log analytics use cases. Longer data retention windows provide deeper access to retrospective log data but are more costly and may degrade ELK stack performance. Ultimately, log data retention windows fail when they limit the ability to get the maximum value from logs, leading to lost insights and prevented use of valuable use cases for retrospective log data. A centralized log management tool that enforces a small storage footprint and supports long-term security, DevOps, and cloud log analysis use cases is needed to gain total observability.