Glaspoort, a fiber infrastructure operator in the Netherlands, has transformed its data operations by integrating Databricks' Lakebase to enable database branching similar to Git. This approach addresses critical challenges in testing, deployment, and maintaining environment consistency for their OLTP systems supporting a custom project management application.
- Git-style branching isolates OLTP database changes per PR
- Instant, copy-on-write Lakebase branches maintain production data freshness
- Combines transactional and analytical data workflows for operational insights
Infrastructure signal
Glaspoort’s use of Databricks Lakebase represents a novel infrastructure pattern blending transactional OLTP and analytical lakehouse capabilities. Lakebase’s copy-on-write branching enables multiple isolated database environments to coexist efficiently without duplicating data. Each branch provides a fully independent endpoint, supporting CI/CD workflows where each pull request gets its own complete and fresh test database environment.
This architecture reduces risks of environment drift and invalid test results that commonly plague shared development databases. However, it also introduces operational constraints—such as the need to delete dependent child branches before resetting parent branches—which require careful workflow design. Overall, it demonstrates a scalable model for modern cloud infrastructure where database and application lifecycles are tightly integrated using software engineering best practices.
Developer impact
Developers benefit from faster, more reliable feedback loops since every feature branch can be tested against an isolated database instance that mirrors production state. They avoid conflicts that arise from multiple developers pushing changes into the same shared environment, reducing the chance of compounded errors going unnoticed until deployment.
Furthermore, integrating transactional OLTP data with self-service analytics and AI/BI dashboards improves visibility into system behavior and project opportunities. Developers can focus more on building features and insightful metadata rather than maintaining brittle one-off reports. This integration supports a more agile, data-driven developer workflow aligned with modern DevOps and CI/CD practices.
What teams should watch
Teams adopting this branching pattern should monitor branch dependencies carefully since parent branches cannot be reset without first deleting their child branches. This constraint requires coordinated branch lifecycle management to prevent deadlock and stale data issues in long-running feature branches.
Operational teams should track the cost implications as the number of branches scales and evaluate strategies to automate branch cleanup and permission management. Additionally, teams integrating OLTP with analytics must ensure consistent data governance and quality testing to maintain trust in both systems.
Finally, teams should follow ongoing platform capabilities of Lakebase branching and CI/CD tools to optimize observability, deployment safety, and environment synchronization—especially for distributed teams working on high-impact infrastructure projects like fiber networks.