Cloud platforms, microservices and Kubernetes have multiplied the logs, metrics and traces every organisation produces. Storage bills rise, analysts drown in noise and every tool gets slower. A telemetry pipeline puts you back in control of what goes where.
Why log management costs keep rising
- More sources: every new service, cluster and SaaS app adds telemetry.
- Chatty cloud logs: flow logs, Kubernetes logs and cloud audit trails grow quickly with usage.
- Low-value bulk: a large share of machine data is never searched, alerted on or reported.
- Duplicate destinations: the same data is often shipped to several tools at full volume.
What an observability pipeline does
A telemetry pipeline sits between sources and destinations such as your SIEM, security data lake and monitoring tools. It parses, filters, enriches and routes each event to where it delivers value. Public guidance from CISA and NIST supports the same pattern: separate collection from storage, preprocess early, prioritise security-relevant data and manage the data lifecycle.
Five vendor-neutral strategies
- Filter routine, low-value events such as sign-outs, health checks and repetitive metadata before they reach paid tools.
- Reduce redundancy and noise by deduplicating records and dropping verbose debug output that slows analysts and inflates false positives.
- Route by purpose: identity, endpoint and network security events to the SIEM; high-volume operational logs to cheaper tiers.
- Normalize schemas and prune fields with OCSF, ECS or OpenTelemetry conventions, so queries run faster and storage shrinks.
- Tier retention: hot for days, warm for weeks, cold archive for compliance-required logs.
The impact
- Telemetry volume to paid tools often falls by half or more, depending on the source mix.
- Investigations get faster because analysts search cleaner, normalized data.
- Tool choice gets easier: with routing in the pipeline, you can add or replace destinations without re-onboarding sources.
A 30-day implementation plan
- Find the high-volume sources. Baseline GB/day and cost; a handful of sources usually drive most spend.
- Classify logs by value, using detection mapping and CISA ingestion priorities.
- Deploy pipeline preprocessing: filtering, deduplication and enrichment before data reaches the SIEM or observability tools.
- Move archive logs to cold storage to meet compliance without inflating the SIEM.
- Track KPIs weekly: volume reduction, cost per TB, alert fidelity and analyst workload.
Want to see where a pipeline fits? See our security data pipeline reference architecture.
Frequently asked questions
Does filtering reduce incident visibility?
No. Filter only routine, low-risk events from the SIEM copy, and archive full-fidelity logs in cold storage for forensics.
How do we avoid losing critical signals?
Map logs to MITRE ATT&CK detections, prioritise security-relevant sources and keep every event that a real-time detection needs in hot storage.