Monitor PostgreSQL data quality and generate remediation alerts with Slack

Autonomous PostgreSQL Data Quality Monitoring & Remediation

Overview

This workflow automatically monitors PostgreSQL database data quality and detects structural or statistical anomalies before they impact analytics, pipelines, or applications.

Running every 6 hours, it scans database metadata, table statistics, and historical baselines to identify:

- Schema drift - Null value explosions - Abnormal data distributions

Detected issues are evaluated using a confidence scoring system that considers severity, frequency, and affected data volume. When issues exceed the defined threshold, the workflow generates SQL remediation suggestions, logs the issue to an audit table, and sends alerts to Slack.

This automation enables teams to proactively maintain database reliability, detect unexpected schema changes, and quickly respond to data quality problems.

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