Autonomous PostgreSQL Data Quality Monitoring & Remediation
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. How It Works 1. Scheduled Monitoring A Schedule Trigger starts the workflow every 6 hours to run automated database quality checks. 2. Metadata & Statistics Collection The workflow retrieves important metadata from PostgreSQL: Schema metadata** from information_schema.columns Table statistics** from pg_stat_user_tables Historical baselines** from a baseline tracking table These datasets allow the workflow to compare current database conditions against historical norms. 3. Data Quality Detection Engine Three parallel detection checks analyze the database: Schema Drift Detection Identifies new tables or columns Detects removed columns or tables Detects datatype or nullability changes Null Explosion Detection Calculates null percentage per column Flags columns exceeding configured null thresholds Outlier Distribution Detection Compares current column statistics against historical baselines Uses statistical deviation (z-score) to detect abnormal distributions 4. Issue Aggregation & Confidence Scoring All detected issues are aggregated and evaluated using a confidence scoring system based on: Severity of the issue Data volume affected Historical frequency Consistency of detection Only issues above the configured confidence threshold proceed to remediation. 5. SQL Remediation Suggestions For high-confidence issues, the workflow automatically generates SQL investigation or remediation queries, such as: ALTER TABLE fixes NULL cleanup queries Outlier review queries 6. Logging & Alerting Confirmed issues are: Stored in a PostgreSQL audit table Sent as alerts to Slack 7. Baseline Updates Finally, the workflow updates the data quality baseline table, improving anomaly detection accuracy in future runs. Setup Instructions Configure a PostgreSQL credential in n8n. Replace <target schema name> in the SQL queries with your database schema. Create the following tables in PostgreSQL: Audit Table data_quality_audit Stores detected data quality issues and remediation suggestions. Baseline Table data_quality_baselines Stores historical statistics used for anomaly detection. Configure your Slack credential. Replace the placeholder Slack channel ID in the Send Alert to Team node. Optional configuration parameters can be modified in the Workflow Configuration node: confidenceThreshold maxNullPercentage outlierStdDevThreshold auditTableName baselineTableName Use Cases Database Reliability Monitoring Detect unexpected schema changes or structural modifications in production databases. Data Pipeline Validation Identify anomalies in datasets used by ETL pipelines before they propagate errors downstream. Analytics Data Quality Monitoring Prevent reporting inaccuracies caused by missing data or abnormal values. Production Database Observability Provide automated alerts when critical database quality issues occur. Data Governance & Compliance Maintain a historical audit log of database quality issues and remediation actions. Requirements This workflow requires the following services: PostgreSQL Database** Slack Workspace** n8n** Nodes used: Schedule Trigger Set Postgres Code (Python) Aggregate IF Slack Key Features Automated database health monitoring Schema drift detection** Null explosion detection** Statistical anomaly detection** Confidence-based issue filtering Automated SQL remediation suggestions Slack alerting Historical baseline learning system Summary This workflow provides an automated data quality monitoring system for PostgreSQL. It continuously analyzes schema structure, column statistics, and historical baselines to detect anomalies, generate remediation suggestions, and notify teams in real time. By automating database quality checks, teams can identify issues early, reduce debugging time, and maintain reliable data pipelines. An n8n automation workflow template by ResilNext.
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