Collector Deployment Strategy

Collector Deployment Strategy

A key pillar of the observability framework’s architecture is the standardized and automated deployment of telemetry collectors across heterogeneous infrastructure — cloud-native, on-prem OT systems. This section outlines the collector types and deployment methods used across the enterprise to ensure complete, consistent, and secure telemetry ingestion.

To ensure consistency, scalability, and repeatability across environments, observability components are managed through robust CI/CD pipelines. This eliminates manual intervention, reduces configuration drift, and enables automated rollout of telemetry infrastructure and configurations.

Deployment Strategy by Infrastructure Scope

Scope	Collector Type	Deployment Method
Azure Virtual Machines	Grafana Alloy (Prometheus mode, Loki logs)	Terraform (infra) + GitHub Actions (agent config)
Azure Kubernetes Service	Grafana Alloy as DaemonSet/sidecar (metrics/logs/traces)	Helm Charts + GitHub Actions
Azure Functions & Container Apps	OpenTelemetry SDK → OTel Collector → Grafana Alloy	SDK instrumentation + GitHub Actions
Azure Data Services	Azure Monitor integration (SQL MI, Cosmos DB) + OTel	Native plugin configuration via Terraform
DevOps Tooling	Azure Pipelines: Metrics via Azure Monitor
GitHub Actions: Prometheus Exporter	Native integrations + GitHub Actions setup
On-Prem OT Infrastructure	Node Exporter, WMI Exporter, OTel Collector, Grafana Alloy	Ansible (agent deployment + secure transport)
OT Application Services	Windows Exporter, Grafana Alloy	Ansible + central config management

Deployment Principles

• Environment Segregation: Collectors are deployed separately for dev, qa, and prod stacks, using environment-specific API keys and labels.
• Label Metadata Injection: All collectors are configured with metadata such as region, env, plant, and team to support cost attribution, RBAC, and dashboard filtering.
• Secure Data Transmission: OT and on-prem collectors route data through VPN or private link connections to Grafana Cloud.
• CI/CD Integration: All configuration is version-controlled and promoted through GitHub Actions or Terraform pipelines, ensuring reproducibility and auditability.

Notes:

• Grafana Alloy acts as the common ingestion point for logs, metrics, and traces where supported.
• Azure Monitor acts as a native integration layer for PaaS services like SQL MI and Cosmos DB — see Data Source Strategy for the full telemetry-source breakdown.
• DevOps tools export build/run pipeline telemetry either via direct integration (Azure Pipelines) or third-party exporters (GitHub Actions).
• Labels and API keys are scoped by environment and region for cost, access, and data control.

Agent Rollout and Updates via CI/CD

Scope:

• Observability agents (e.g., Prometheus exporters, Grafana Agents, OpenTelemetry Collectors) are deployed and updated using automated pipelines.
• Terraform Cloud and GitHub Actions are used to:
  • Deploy agents to Azure VMs, AKS clusters, and on-prem environments
  • Ensure consistent configuration via templated modules or manifests
• Pipelines include validation, approval gates, and rollback mechanisms to support safe updates and controlled rollouts.

Examples:

• Use of Helm charts and FluxCD or ArgoCD for rolling out collectors to Kubernetes.
• GitHub Actions pipelines that deploy collector config via Terraform apply on PR merge.

Observability Configurations as Code

Scope:

• All observability artifacts — dashboards, alert rules, recording rules, synthetic tests — are stored and maintained as code.
• Tools and standards include:
  • grafana-dashboard-as-code (JSON)
  • alerting-rules.yaml for Prometheus-style alerts
  • synthetics-config.json for synthetic checks
• Configurations are automatically linted, validated, and deployed via CI.

Benefits:

• Enables peer review through pull requests
• Facilitates reproducibility across staging, UAT, and production
• Version history and change tracking improve auditability and accountability

Version-Controlled and Environment-Specific Dashboards & Alerts

Scope:

• Dashboards and alerting rules are tailored per environment (e.g., dev, staging, production) to reflect specific SLOs, data sources, and alert thresholds.
• Separate folders and tagging in Grafana distinguish between environments.
• CI/CD workflows deploy configurations conditionally based on the target environment:
  • e.g., main branch → prod; develop → staging

Design Considerations:

• Sensitive or high-noise alerts are suppressed or throttled in lower environments
• Golden dashboards in prod are locked down for editing; others allow experimentation

Examples:

• A Terraform module provisions environment-specific Grafana folders and assigns RBAC accordingly.
• GitHub Actions uses environment matrix to loop through and apply configurations to each target workspace.