Observability Pipelines
Ingestion, dashboards, and retention that match what the data is worth.
Observability estates grow one urgent decision at a time: a Beats agent here, a Logstash box there, a metrics stream nobody owns. The result works — expensively, fragilely, and with dashboards nobody quite trusts.
We design and build the pipeline deliberately.
Ingestion architecture
- Collector strategy — Elastic Agent with Fleet, Logstash, or an OpenTelemetry Collector (or a deliberate combination), chosen for your sources, transform needs, and team, not by default.
- Transform placement — what happens at the edge, in Logstash or the OTel Collector, and in Elasticsearch ingest pipelines — and why it matters for cost and debuggability.
- Buffering and backpressure — where a queue (often Kafka) belongs, and where it's just another system to run.
- Schema discipline — ECS or OpenTelemetry semantic conventions applied consistently, so fields mean the same thing across every stream and dashboards stop lying.
Dashboards and alerting
Kibana dashboards designed around the questions on-call engineers actually ask, not around what's easy to chart. Alerting rules with thought-through thresholds and routing — the aim is that a page means something, every time.
Retention and cost
Retention is a per-stream engineering decision: debug logs are not audit logs are not SLO metrics. We design ILM policies and data tiers so each stream lives on storage that matches its query patterns — hot for the last days, warm or cold for the tail, frozen and searchable snapshots for the archive — and the delete phase actually deletes.
Where this lands
Typical outcomes are a smaller hot tier, fewer moving parts between the source and the index, dashboards your on-call rotation trusts, and a bill that tracks data value instead of data volume. If you just want the assessment first, start with a cluster and cost review — the two engagements compose naturally.