SME Track: SRE & Infra Ops
Presenter: Specialist (DSR)
Date: 2026-10-14
SLB × Elastic Workshop Program
Ingestion troubleshooting with Streams, ES|QL, and AI Assistant — lab on Serverless, patterns apply everywhere.
These labs run on Observability Serverless — a fully managed project so you can practice without cluster operations.
The same capabilities you explore here — ES|QL, Streams, AI Assistant, Agent Builder, Workflows, and SLOs — are available on Elastic Cloud Hosted (ECH) and self-managed deployments.
Serverless mainly saves operational toil (sizing, ILM, Fleet, upgrades). Your observability skills transfer directly.
Use → to see why each feature matters for SLB.
Send OTLP once — Elastic handles the rest
Multiple exporters, bespoke endpoints, and glue code to normalize Prometheus, Loki, and Jaeger into one query model.
Point OpenTelemetry collectors at Elastic OTLP endpoints — unified logs, metrics, and traces in any deployment model.
Managed routing and processing for telemetry
Custom ingest pipelines, index templates, and rollover policies per team — fragile, hard to govern, and different on every cluster.
Streams define how logs, metrics, and traces are routed, processed, and retained — on Serverless, ECH, and self-managed, with a unified UI.
One query language for logs, metrics, and traces
Different syntax per signal — PromQL for metrics, LogQL for logs, trace UI only — context switching slows incidents.
ES|QL pipes data through filters, stats, and joins across observability datasets in Logs Explorer and Dev Tools.
Natural language over your live telemetry
Every investigator rebuilds the same ES|QL, scrolls dashboards, and writes runbook prose from scratch.
Ask questions in plain language — get ES|QL, summaries, and correlated logs/traces grounded in your project data.
Your lab uses Elastic Observability Serverless for a zero-ops learning environment.
The steps and features are the same on ECH and on-prem — follow the assignment panel when Kibana opens.
Instruqt track: slb-sme-sre-infra-ops