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Data Schema Tracker – Monitor Schema Evolution

AI-Driven Module for Metadata Observability and Data Quality – digna Data Schema Tracker

Purpose

The Data Schema Tracker keeps you informed about how your database structures evolve.
It continuously monitors table schemas, columns, and datatypes to detect schema drift — intentional or unintentional structural changes that can disrupt pipelines, ETL jobs, or BI dashboards.

By ensuring transparency in schema evolution, digna helps organizations maintain trust in the quality of data, uphold observability of data systems, and avoid costly production incidents caused by undetected schema changes.

Technical Overview

What It Monitors

  • Added or Removed Columns – Detects newly introduced, renamed, or deleted columns.
  • Datatype Modifications – Identifies changes such as INT → VARCHAR or DATE → TIMESTAMP.
  • Table and View Modifications – Tracks creation, renaming, or removal of tables and views.
  • Cross-Environment Differences – Compares schema versions between Dev, Test, and Production environments.

Detection & Alerting

  • Scans database metadata or system catalogs directly within your data platform.
  • Compares each schema snapshot with the previously known version stored in digna’s observability schema.
  • Generates real-time alerts in the dashboard, via API, or external notification channels (email, Slack, webhook).
  • Logs every schema version for historical tracking and audit readiness.

Architecture and Execution

  • In-Database Execution: digna runs entirely within your environment, querying metadata views without extracting any data.
  • Lightweight Scanning: accesses only structural information — never user data.
  • Centralized Storage: schema metadata and drift records are stored in the digna observability schema for visualization and analytics.
  • Automation: supports scheduled or event-based scans via digna Core or external orchestration tools.

Example Use Cases

Use Case Description
ETL Stability Monitoring Detect upstream structure changes before pipelines fail due to schema mismatches.
Business Intelligence Reliability Prevent broken dashboards caused by renamed or missing columns.
Data Warehouse Governance Maintain an auditable history of schema evolution for compliance and impact analysis.
Integration Oversight Ensure that data lake and warehouse schemas remain synchronized after structural updates.

Benefits

Area Benefit
Data Quality Prevents undetected schema drift that can corrupt or invalidate data pipelines.
Observability Adds structural monitoring to the overall observability of data ecosystems.
Compliance Maintains versioned schema history for audit, traceability, and change control.
Prevention Detects structural issues before they cascade into reporting or production errors.

How It Works

  1. Snapshot Collection – digna captures the current schema metadata.
  2. Comparison – the new snapshot is compared