Pipeline Observability Setup (QA)

Pipeline Observability Setup (QA)

1. 01

   Define the critical data assets and their owners for each client pipeline

   List the tables, models, or feeds that the client's reporting depends on, and assign a named owner for each. This mirrors the asset-centric approach used by platforms like Dagster, where lineage and ownership are tracked explicitly.

2. 02

   Instrument every transformation step with logging and metric capture

   Ensure each ETL or ELT job records row counts, timestamps, and error rates. Tools like Astronomer's Airflow or Coalesce can expose these signals natively, but the key is to standardize the format across all pipelines.

3. 03

   Set up automated data quality checks that run on every schedule

   Implement tests for null rates, uniqueness, and referential integrity. For example, dbt tests can be embedded in transformation code, while platforms like Databricks offer built-in quality monitoring. The goal is to catch anomalies before they reach client dashboards.

4. 04

   Create a centralized observability dashboard that aggregates pipeline health

   Pull logs and metrics from all orchestration and transformation tools into a single view. This dashboard should show at a glance which pipelines are failing, which are at risk, and which are healthy, enabling rapid triage.

5. 05

   Establish alerting rules with severity levels and escalation paths

   Define what constitutes a warning versus a critical failure, and who gets notified at each level. For instance, a row-count drop of 20% might page the delivery lead, while a full pipeline failure alerts the client success manager.

6. 06

   Document the runbook for common failure modes and remediation steps

   Write down the top five failure scenarios (e.g., API rate limits, schema changes, credential expiry) and the exact steps to resolve each. This runbook should be accessible to any team member on call.

7. 07

   Review observability coverage with the client on a monthly basis

   Walk through the dashboard and alert history with the client to confirm that the monitored metrics align with their business priorities. This builds trust and ensures the agency is accountable for data delivery.