What is data sampling or filtering at ingest, and why does it matter for cost? [Intermediate]
Answer
Ingest sampling or filtering reduces the amount of data sent to the backend by dropping, transforming, or sampling events before indexing. It matters because high-volume low-value telemetry drives license, storage, and search cost.
Technical explanation
Filtering removes events that are not useful, such as successful health checks or repetitive debug logs.
Sampling keeps a representative subset, useful for high-volume success events but risky for rare errors.
Never sample compliance, security, audit, or error data unless the business has explicitly approved it.
Hands-on example
Example policy: keep 100 percent of ERROR and WARN logs, keep 100 percent of audit events, sample successful access logs at 10 percent for high-volume endpoints, and drop Kubernetes readiness/liveness probe logs at ingestion. Validate savings and investigation impact monthly.
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