Journey from Broken Dashboards to O11y Maturity
summary
Claims a concrete outcome: an observability QA framework that embeds Great Expectations, dbt, OpenTelemetry and Prometheus and Grafana into pipelines, adds lineage, fail‑fast gates and continuous checks, and measurably reduces firefighting. Cites a surprising figure — over 30% of data team time lost to data downtime — and shows the system catching schema drift and missing loads in real time, annotating dashboards and preventing KPI outages rather than just promising examples.
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chapters (8)
- 0:00Introduction: The Problem with Data Quality
- 0:35Identifying Data Quality Issues
- 2:08Real-World Data Quality Challenges
- 3:46Traditional QA vs. Modern Observability
- 5:51Implementing Observability in Data QA
- 11:14Tools and Techniques for Data Observability
- 16:08Best Practices and Lessons Learned
- 19:22Call to Action: Improving Your Data QA
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