The Missing Link in Modern Data Pipelines: Liquibase + Databricks

As more data teams adopt the Databricks Data Intelligence Platform for mission-critical analytics, managing changes to SQL and Python code across environments has become a growing challenge. Most teams still rely on manual processes, notebooks, or custom scripts to manage deployments. These approaches slow delivery, introduce risk, and make it difficult to meet growing audit and compliance requirements.

Liquibase and Databricks have partnered to address this challenge. Today, we’re introducing the Liquibase Pro Databricks Extension, which brings automation, version control, and governance to the Databricks SQL Warehouse.

With this new extension, teams can manage database changes with the same rigor, speed, and visibility they apply to application code. This lays the foundation for scalable, secure, and automated pipelines in the lakehouse.

Why It Matters

• Teams need to move faster, but current change processes rely on manual reviews and inconsistent scripting
  
  
• Security, audit, and compliance expectations are increasing across all data platforms
  
  
• Without standard workflows and governance, teams struggle with drift, delays, and deployment errors
  
  
• Developers lack self-service capabilities for data workflows, which slows down innovation
  
  

The Liquibase Databricks Pro Extension addresses these issues with automated change management, enterprise-grade observability, and policy enforcement. It helps teams scale faster and reduce risk while maintaining control and compliance.

What’s Included in v1.0.0

This release brings advanced Liquibase capabilities to Databricks, designed specifically for the lakehouse environment:

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SQL and Python Function Support
Define and deploy user-defined functions through changelogs to improve reuse and maintain consistency

CLONE TABLE and VOLUMES Support
Track clone operations and volume activity to improve data lifecycle governance

Time Travel with RESTORE AS OF
Restore tables to a previous state using Databricks Time Travel to simplify debugging and recovery

Version and Timestamp Tracking
Automatically capture who made what change and when, with full visibility for audits and drift detection

Prepackaged, Production-Ready Extension
Easy to install with no custom setup required. Designed to scale across teams and environments

Liquibase Pro Features
Includes policy checks, changelog validation, audit trails, and enterprise support

Explore the setup guide → Liquibase Databricks Docs
View the connector on GitHub → Liquibase-Databricks

Customer Example: Scaling Secure Change Management at a Global Bank

A global financial services firm recently rolled out a new Databricks-based exchange platform. They needed a unified approach to manage changes across Databricks, SQL Server, and Snowflake while meeting strict governance and audit requirements.

Challenges they faced:

• Inconsistent deployment processes across platforms
  
  
• Duplicate schema changes and limited visibility into who made what change
  
  
• Manual, error-prone policy enforcement
  
  
• Time-consuming audit preparation
  
  

Why they chose Liquibase Pro:

• A single standard across platforms using Liquibase changelogs
  
  
• Prevalidation of changes in clone environments to simulate production rollouts
  
  
• Custom policy enforcement built directly into deployment pipelines
  
  
• Hands-on support from Liquibase engineers for implementation and best practices
  
  

Within 10 weeks, the team replaced fragmented tooling with Liquibase, onboarded developers through Liquibase Academy, and improved delivery speed and compliance. They are now expanding the rollout to additional teams and platforms.

How It Works Across Environments

The Liquibase Databricks Pro Extension enables environment-specific deployments using a shared changelog and property files. This approach simplifies workflows and ensures consistency from development through production.

This example shows how Liquibase can manage changes across two environments—iot_dashboard_dev and iot_dashboard—in Databricks SQL using Unity Catalog. It allows teams to automate changes while maintaining environment-specific configuration and traceability.

The Outcomes You Can Expect

• Versioned, auditable deployments of SQL and Python changes
  
  
• Automated, repeatable workflows across development, staging, and production
  
  
• Built-in governance with changelog validation and audit trails
  
  
• Integration with CI and CD pipelines for consistent releases
  
  
• Fewer manual steps, faster time to market, and stronger security posture
  
  

Liquibase helps teams deliver secure, automated, and compliant database changes at scale without adding complexity.

Get Started

Want to see it in action?

• Read the Databricks engineering blog
  
  
• Explore the Liquibase Databricks setup guide
  
  
• Download the extension on GitHub
  
  
• Watch the Liquibase Extension for Databricks demo
  
  

Ready to explore how this can work in your environment?

Request a custom demo to see how Liquibase can help your team manage database changes in Databricks with automation, control, and confidence.