Monitor PostgreSQL data quality and generate remediation alerts with Slack
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This workflow has 15 nodes and runs on a schedule (every 6 hours). Along the way it connects Postgres and Slack.
How it runs
Trigger: On a schedule (every 6 hours).
- Workflow Configuration (Edit Fields (Set)): sets 5 fields: confidenceThreshold, maxNullPercentage, outlierStdDevThreshold…
- Get Schema Metadata (Postgres): run a query
- Get Table Statistics (Postgres): run a query
- Get Historical Baselines (Postgres): run a query
- Detect Schema Drift (Code): runs custom Python code
- Detect Null Explosions (Code): runs custom Python code
- Detect Outlier Distributions (Code): runs custom Python code
- Combine All Issues (Aggregate): combines items into one
- Calculate Confidence Scores (Code): runs custom Python code
- Check Confidence Threshold (If): checks a condition and branches into true / false
…plus 4 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (7)
- Schedule Trigger
n8n-nodes-base.scheduleTrigger - Edit Fields (Set)
n8n-nodes-base.set - Postgres
n8n-nodes-base.postgres - Code
n8n-nodes-base.code - Aggregate
n8n-nodes-base.aggregate - If
n8n-nodes-base.if - Slack
n8n-nodes-base.slack
From the author
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…