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AI Playbook: 10 - Observability and Alerting

This playbook provides a framework for establishing comprehensive observability into the CDF solution. It covers the creation of monitoring dashboards, the definition of key performance indicators (KPIs), and the configuration of an alerting strategy to ensure operational health.

Goal: To move from a reactive to a proactive operational model by providing deep insights into the performance and health of the entire data pipeline and data model.

1. The Three Pillars of Observability

A robust observability strategy is built on three pillars. Our goal is to capture all three and present them in a unified dashboard.

  1. Metrics: Numerical measurements of system health over time (e.g., data_ingestion_latency_seconds, active_users, failed_transformations_total).
  2. Logs: Detailed, timestamped records of specific events (e.g., [ERROR] Failed to connect to source system XYZ).
  3. Traces: A complete view of a single request or data flow as it moves through the various components of the system (e.g., from extractor to RAW, through transformation, to the final data model).

2. Defining a Dashboard-as-Code Strategy

Just as our data model is defined in code, so too should our monitoring dashboards. This ensures they can be version-controlled, reviewed, and deployed consistently across environments.

  • Action: Define dashboard layouts and queries in a configuration file (e.g., JSON or YAML), which is then stored in the project repository under a dashboards/ directory.
  • Tooling: Use a tool that supports a "dashboard-as-code" approach, such as Grafana (with its JSON model), Datadog (with its Terraform provider), or a similar platform.
  • Deployment: The CI/CD pipeline should include a step to automatically apply any changes to these dashboard configuration files to the monitoring platform.

Example: Grafana Dashboard JSON

{
  "title": "CDF Project Health",
  "panels": [
    {
      "title": "Data Ingestion Latency (p95)",
      "type": "timeseries",
      "targets": [
        {
          "expr": "histogram_quantile(0.95, sum(rate(ingestion_latency_seconds_bucket[5m])))"
        }
      ]
    },
    {
      "title": "Failed Transformations (last 24h)",
      "type": "stat",
      "targets": [
        {
          "expr": "sum(increase(failed_transformations_total[24h]))"
        }
      ]
    }
  ]
}

3. Key Performance Indicators (KPIs) to Monitor

The dashboard should provide at-a-glance visibility into the most critical health metrics.

Data Pipeline KPIs

  • End-to-End Latency: Time from when data is created in the source system to when it is available in the CDF data model.
  • Extractor Throughput: Volume of data being processed by each extractor (e.g., rows/sec, MB/sec).
  • Transformation Errors: Rate of failed transformation runs.
  • Data Freshness: The most recent timestamp for critical data sets.

Solution KPIs

  • API Query Latency: Response time for key API queries against the data model (p95, p99).
  • Active Users: Number of unique users or applications querying the data.
  • Data Quality Score: Percentage of data quality tests that are passing.

4. Alerting Strategy

Alerts are configured on top of the most critical KPIs to proactively notify the team of issues.

  • Action: Define alert rules in code alongside the dashboards.
  • Best Practices:
  • Tiered Severity: Use different alerting channels for different severities (e.g., CRITICAL -> PagerDuty, WARNING -> Slack).
  • Actionable Alerts: Every alert notification should include a link to the relevant dashboard and a brief "what to do" guide.
  • Avoid Flapping: Set appropriate thresholds and time windows to prevent alerts from firing and resolving in rapid succession.

Example Alert Rule (Prometheus/Alertmanager format)

groups:
  - name: CdfAlerts
    rules:
      - alert: HighTransformationFailureRate
        expr: sum(rate(failed_transformations_total[1h])) > 5
        for: 10m
        labels:
          severity: critical
        annotations:
          summary: "High rate of transformation failures"
          description: "More than 5 transformations have failed in the last hour. Check the CDF monitoring dashboard."

Note: This playbook provides a strategic guide. The specific implementation requires selecting a monitoring and alerting stack and instrumenting the various components of the CDF solution to expose the necessary metrics, logs, and traces.