As software lifecycles move faster, the database needs to be able to keep up. Practices such as version controlled migration scripts and iterative schema evolution provide the necessary mechanisms to ensure that your data layer is as agile as your application. Pramod Sadalage saw the need for these capabilities during the early days of the introduction of modern development practices and co-authored a book to codify a large number of patterns to aid practitioners, and in this episode he reflects on the current state of affairs and how things have changed over the past 12 years.
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- Your host is Tobias Macey and today I’m interviewing Pramod Sadalage about refactoring databases and integrating database design into an iterative development workflow
- How did you get involved in the area of data management?
- You first co-authored Refactoring Databases in 2006. What was the state of software and database system development at the time and why did you find it necessary to write a book on this subject?
- What are the characteristics of a database that make them more difficult to manage in an iterative context?
- How does the practice of refactoring in the context of a database compare to that of software?
- How has the prevalence of data abstractions such as ORMs or ODMs impacted the practice of schema design and evolution?
- Is there a difference in strategy when refactoring the data layer of a system when using a non-relational storage system?
- How has the DevOps movement and the increased focus on automation affected the state of the art in database versioning and evolution?
- What have you found to be the most problematic aspects of databases when trying to evolve the functionality of a system?
- Looking back over the past 12 years, what has changed in the areas of database design and evolution?
- How has the landscape of tooling for managing and applying database versioning changed since you first wrote Refactoring Databases?
- What do you see as the biggest challenges facing us over the next few years?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Database Refactoring
- Martin Fowler
- Agile Software Development
- XP (Extreme Programming)
- Continuous Integration
- Test First Development
- DDL (Data Definition Language)
- DML (Data Modification Language)
- ORM (Object Relational Mapper)
- ODM (Object Document Mapper)
- Document Database
- Unit Testing
- Integration Testing
- OLAP (On-Line Analytical Processing)
- OLTP (On-Line Transaction Processing)
- Data Warehouse
- QA==Quality Assurance
- HIPAA (Health Insurance Portability and Accountability Act)
- PCI DSS (Payment Card Industry Data Security Standard)
- Polyglot Persistence
- Toplink Java ORM
- Ruby on Rails
- ActiveRecord Gem