Anypoint Insights & Observability (Course #observability)
For ops, admins, architects.
By the end, you can
By the end of this course, you will be able to instrument the platform end to end — dashboards, custom metrics, distributed traces, calibrated alarms — so detection falls from 25 minutes to under 5 within telemetry budget.
Running case study
Kestrel Health runs 60 Mule integrations for patient scheduling but flies blind: alerts fire on CPU alone, detection averages 25 minutes, and postmortems lack trace evidence. The SRE guild must stand up full observability in one quarter — golden-signal dashboards, trace-linked alarms, exported telemetry — with ingestion capped at 15 GB/day.
Programme every line is something you do
5 modules · 13 topics · in course-flow order
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Module 1
Dashboards that decide
Objective. Build golden-signal dashboards with custom metrics and alarm rules engineered to cut noise.
- Compose dashboards around golden signals — latency p95, error rate, saturation — per application group
- Define custom metrics from business events (orders/min, claims/hour) next to JVM gauges
- Calibrate alarms with warning/critical bands and dedup windows (noise target: −70% alerts)
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Module 2
From analytics to KPIs
Objective. Turn cross-environment analytics into trend baselines and executive-ready business KPIs.
- Aggregate API analytics across environments into week-over-week baselines on volume and errors
- Rank APIs by SLO burn rate to focus tuning where consumers feel degradation first
- Derive business KPIs (claims submitted/hour) from usage analytics and publish them on an exec view
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Module 3
One trace, whole story
Objective. Follow a single message across API-led layers by trace ID and bind matching log evidence.
- Read span waterfalls across Experience→Process→System layers to localize added latency
- Join logs to traces by correlationId (join-rate target: 100%) so every span carries its evidence
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Module 4
Telemetry without lock-in
Objective. Stream metrics and logs to Splunk/Datadog/Grafana through an export design you can price.
- Export metrics and logs to Splunk, Datadog or Grafana, sizing ingest first (cap: 15 GB/day)
- Evaluate OpenTelemetry collectors as the normalization layer for mixed Mule and non-Mule sources
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Module 5
Before the pager fires
Objective. Shift operations left with error budgets, anomaly alerts and capacity forecasts.
- Monitor SLAs as error budgets (99.9% availability ≈ 43 min/month of tolerated downtime)
- Tune anomaly detection on seasonal traffic to catch drift before consumers escalate
- Forecast worker capacity six weeks ahead from insights trends (scale before saturation)
References & Official Documentation every claim above has a source
Module 1
- Getting Alerts in Anypoint MonitoringMuleSoft Docs
- Setting Up Custom DashboardsMuleSoft Docs
Module 2
- Mule API AnalyticsMuleSoft Docs
Module 3
- W3C Trace ContextSpecification
- Correlation IDMuleSoft Docs
- Examining TracesMuleSoft Docs
Module 4
- Exporting Telemetry Data to Third-Party Monitoring SystemsMuleSoft Docs
- OpenTelemetry Support in Mule RuntimeMuleSoft Docs