Liquibase and Databricks are partnering to bring modern change management to the Lakehouse.
Database DevOps firm Liquibase announced the launch of the Liquibase Pro Extension for Databricks, a new integration with the Databricks Data Intelligence Platform designed to help data and platform teams manage schema changes in Databricks SQL with greater speed, structure and confidence.
A data Lakehouse is a unified data platform that combines the best features of both data lakes and data warehouses, offering a single repository for structured, semi-structured and unstructured data.
Why Liquibase and Databricks are bringing change management to the Lakehouse
As more organizations rely on Databricks to power mission-critical analytics, artificial intelligence (AI) workloads and production-grade data products, database change has become a growing challenge, according to a press release. Most teams still rely on notebooks, SQL scripts and ticket-based processes to make updates. These manual workflows introduce risk, increase complexity and delay delivery.
“Data teams need to move faster and without putting their business at risk. Liquibase Pro delivers exactly that, bringing structured change management to the Lakehouse so teams can accelerate delivery, safely,” said Kevin Chappell, VP of strategic partnerships at Liquibase. “Liquibase Pro gives teams a repeatable, safe way to deliver database changes with the same speed and discipline they expect everywhere else in their stack.”
Helping teams maintain control, auditability and compliance
With its new offering, Liquibase aims to bring change automation directly into the Databricks environment, helping teams move faster while maintaining control, auditability and compliance, the firm stated.
Key capabilities include:
- Version-controlled deployments of SQL objects and Python-based UDFs.
- Support for Unity Catalog, Time Travel, volumes and CLONE TABLE operations.
- Environment-specific configuration to prevent drift across dev, staging and production.
- Built-in changelog validation, policy enforcement and audit trails.
- Elimination of notebook and script-based deployment workflows.
“As more organizations standardize on Lakehouse architecture for analytics and AI workloads, managing schema changes consistently becomes a critical part of delivering trusted data intelligence,” said Ariel Amster, director, strategic technology partners at Databricks. “This solution from Liquibase helps our customers simplify change management while benefiting from the Databricks Data Intelligence Platform’s best-in-class governance capabilities.”
Change management in the AI era
Change management has never been more crucial than in the current AI era. AI adoption is skyrocketing – driven by new advancements in generative AI and agentic AI. Organizations are investing heavily in AI technology to support wide-ranging business use cases.
Behind the tech and hype, change management is the crucial factor in achieving effective, lasting success from AI adoption. Carefully managing the vast amount of change AI can introduce is essential. As businesses increasingly integrate AI into corporate workflows, change management strategies must evolve to harness the transformative potential of these technologies.
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