Kevin Hartman
Kevin Hartman leads the Partner Solutions Architects team at Databricks, who help consulting partners build Data and AI solutions on the Databricks platform.
Kevin has been solving complex problems using technology and data for over 3 decades. His specialty is formulating high-impact solutions that leverage emerging technology innovations. He has inspired, influenced, and led the creation of hundreds of digital products — working from both inside and alongside teams of researchers, scientists, designers, engineers, and enthusiasts.
Kevin has a bachelors degree in Computer Engineering from the University of Illinois and a masters degree in Information and Data Science from UC Berkeley, and invited as faculty member to the later program. He is based in Chicago.
Session
For years, teams have treated application code as something that can be safely branched, tested, reviewed, and merged. Databases, however, have often remained harder to evolve. Even with migrations, automated tests, and disciplined delivery practices, many teams still struggle to experiment safely with database changes, validate refactorings against realistic data, and collaborate effectively across developers, DBAs, and platform teams.
This talk builds on the foundations of Evolutionary Database Design and applies the framework of values, principles, methods, practices, and tools to an emerging capability: database branching. New approaches from technologies such as Lakebase, Neon, PlanetScale, Supabase, and others are making it possible to create isolated database branches that fit modern development workflows, in some cases as full-fidelity, copy-on-write clones of production-scale data.
We will explore how database branching changes the developer loop: start work, develop, test, merge, and deploy to the next environment. When teams can create realistic database branches quickly and safely, database refactoring becomes less risky, feedback loops become shorter, and experimentation becomes part of everyday delivery.
The session will focus on three key shifts:
- Self-sufficiency: enabling teams to work more independently without waiting for shared database environments.
- Collaboration: moving DBAs and data specialists from gatekeepers to active design contributors.
- Innovation: allowing teams to run more experiments, compare alternatives, and discover better database designs before committing to production paths.
Attendees will leave with a practical understanding of how database branching supports DevOps principles, improves flow, and opens new possibilities for evolutionary database development.