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Key takeaways

What you learned

  1. Data sources connect Grafana to external systems: query, visualize, and create alerts on data where it already lives

  2. Common data sources by use case:

    • Metrics: Prometheus, InfluxDB
    • Logs: Loki, Elasticsearch
    • Cloud: CloudWatch
    • Application data: MySQL, PostgreSQL
    • Flexible: Infinity, Google Sheets

  3. The critical trade-off:

    • Data Sources = Query, visualize, alert (same alerting engine as integrations; some include pre-built dashboards)
    • Integrations / Send Telemetry = Full Grafana Cloud features (ML insights, SLOs, correlations)

The one thing to remember

Data sources let you query, visualize, and alert. Same alerting engine, same dashboarding capabilities. The trade-off: data stays external, so you won’t get ML-powered insights, SLOs, or cross-signal correlations.

Script

Let’s recap what you’ve learned. Data sources connect Grafana to external systems. They’re how you query, visualize, and alert on data that lives elsewhere.

Different data sources are optimized for different purposes: Prometheus and InfluxDB for metrics, Loki and Elasticsearch for logs, CloudWatch for AWS, MySQL and PostgreSQL for application data, Infinity and Google Sheets for flexible scenarios.

The critical trade-off to remember: data sources let you query external data and create alerts, and some even include pre-built dashboards. But your data stays external, so you miss out on Grafana Cloud features like ML-powered anomaly detection, SLOs, and cross-signal correlations.

Data sources are the right choice when you have existing infrastructure, data sovereignty requirements, or specific systems that need connecting. Choose based on your situation.

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