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Database Drift Detection & Schema Monitoring

Atlas Schema Monitoring provides a set of tools and features to help you manage, monitor, and detect drift in your database schema effectively. Teams can either deploy an agent (container) in their database VPC or use a GitHub Action to track schema changes and report metadata to the Atlas Cloud control plane. Using this metadata, Atlas provides:

  1. Live visibility of your database schema with automated ER diagrams and auto-generated documentation.
  2. A Changelog of all schema changes, so you can see how schemas change over time, and easily triage schema change related issues.
  3. Alerts Use Webhooks or Slack notifications to inform or alert teams that need to know about schema changes or drift detected.

GitHub Actions Quickstart

Get visibility into your database schema in under 5 minutes using GitHub Actions.

GitLab CI/CD Quickstart

Get visibility into your database schema in under 5 minutes using GitLab CI/CD.

BitBucket Pipelines Quickstart

Get visibility into your database schema in under 5 minutes using Bitbucket Pipelines.

Agent Quickstart

Get visibility into your database schema in under 5 minutes using the Atlas agent.

Live Demo

See it in action with a live demo.

Drift Detection

Automatically monitor schema drifts.

Webhooks

Set up webhooks to receive notifications about schema changes and drifts.

Getting Started with Atlas Cloud

Learn how to set up and start using Atlas Cloud for your projects.

What features are essential in a schema monitoring tool?​

A schema monitoring tool has to answer five questions about every database it watches:

  1. What is the schema right now? The tool inspects the live database and keeps a current picture of it, including objects such as views, triggers, and functions.
  2. What changed, and when? Changes are recorded in a changelog, so an incident can be traced back to the schema change that caused it.
  3. Does the database still match the source of truth? Drift detection compares the live schema against the migration directory or schema repository and reports the difference.
  4. Who gets told? Webhooks and Slack notifications push change and drift events to the team that owns the database, instead of waiting for someone to open a dashboard.
  5. How big is it, and how is it growing? Alongside schema snapshots, the agent collects table statistics for every monitored database, configurable per database or schema.

Two implementation details matter as much as the feature list: the tool should only read schema metadata and never the data in your tables, and it should run inside your network instead of asking you to open the database to a vendor. Atlas does both. The agent runs in your environment and connects to the database directly, with credentials that never reach the control plane, or a GitHub Action reports the same metadata from your CI pipeline.

Overview​

How it Works

Learn about the key concepts and features of Atlas Database Schema Monitoring.

Database Credentials and Security

Discover how to securely provide database credentials to the Atlas agent.

Configure Monitoring via Code

Configure Atlas Agent monitoring using HCL configuration files for version-controlled, declarative monitoring.

Deployment Guides​

Helm

Deploy the Atlas agent using Helm on Kubernetes.

Terraform (AWS)

Deploy the Atlas Agent to AWS using Terraform.