Recon tool for cloud provider attribution. Supports AWS, Azure, Google, Cloudflare, and Digital Ocean. - iknowjason/edge

## The Problem with Click-Ops Dashboards Your team has 200 dashboards. You don't know who owns them. Half are broken. The rest show yesterday's reality. This is click-ops debt, and it compounds faster than code debt. ## Observability as Code Every dashboard, alert, and SLO definition should live in a Git repository alongside your service code. resource "datadog_dashboard" "api_gateway" { title = "API Gateway - Golden Signals" description = "Owner: @platform-team" layout_type = "ordered" widget { timeseries_definition { title = "Request Rate (per second)" request { q = "sum:api.requests{service:gateway}.as_rate()" } } } widget { timeseries_definition { title = "P99 Latency" request { q = "max:api.latency{service:gateway}.as_count()" } } } } This lives next to `main.tf` for your service. When you deploy the service, you deploy the observability. ## Benefits That Compound **1. Ownership is clear.** The file has a CODEOWNERS entry. PRs require review. **2. Dashboards auto-update.** Renaming a service? Terraform refactor propagates to all dashboards. **3. Drift detection.** Someone clicked "save as" in the UI and now that dashboard is out of sync. `terraform plan` catches it. **4. Review before production.** Alert changes go through PR review. No more "who set this threshold?" ## Tooling by Platform datadog: provider: DataDog/datadog resources: datadog_monitor, datadog_dashboard, datadog_slo grafana: provider: grafana/grafana resources: grafana_dashboard, grafana_alert_rule prometheus: approach: YAML files in Git, deployed by ArgoCD resources: alert rules, recording rules new_relic: provider: newrelic/newrelic resources: newrelic_alert_policy, newrelic_dashboard Pick one source of truth. Don't mix. ## A Real Example We have a module that takes a service name and generates a complete observability stack: module "service_observability" { source = "./modules/observability" service_name = "payment-processor" team_slack = "#payments" severity_map = { error_rate_pct = 1.0 p99_latency_ms = 500 saturation_pct = 80 } slo_targets = { availability = 0.9995 latency_p99 = 0.99 } } One module call creates: 3 dashboards, 8 alerts, 2 SLOs, a Slack channel binding, and a PagerDuty escalation policy. ## The Hardest Part The code is easy. The hard part is: 1. **Migrating existing click-ops dashboards** — budget 2 weeks 2. **Getting engineers to edit YAML/HCL instead of the UI** — budget 3 months of reminders 3. **Blocking UI edits** — some tools let you set dashboards to read-only 4. **Reviewing alert changes** — PR reviewers need context ## The Anti-Pattern to Avoid Don't write Terraform for every custom chart an engineer wants. That leads to 500-line dashboard modules nobody understands. Instead, define **standard dashboards** (golden signals, RED/USE, SLO burn rate) as modules. Let engineers add their own custom dashboards in the UI if they want, but mark them as "explore-only" (not alert-worthy). Core observability = code. Experimental exploration = UI. ## Migration Strategy Week 1: Pick 1 service, convert its dashboards to Terraform Week 2: Add alerts + SLOs to Terraform Week 3: Delete the UI versions Week 4: Create a module from the patterns Month 2: Roll out to 10 more services Month 3: Require all new services to use the module Six months in, your click-ops debt is gone and your observability is reproducible. **Written by Dr. Samson Tanimawo** BSc · MSc · MBA · PhD Founder & CEO, Nova AI Ops. https://novaaiops.com

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JOPSがAmazon API Gatewayパートナーに認定されたことを報告します。AWSの高い技術力を証明するこの認定は、北陸初の快挙です。