Evolutionary database

If the rate at which business is changing is an indicator of change in requirements, then the days of doing upfront database design are gone. Instead, projects should follow evolutionary database techniques and continue to change their database schemas as new requirements are implemented over the course of the project. Deployment of database changes should also be automated so that the application release that relies on those changes does not have to wait for manual deployment of the database changes. Automated database deployment ensures that application and database changes can be deployed automatically. Evolutionary database and automated database deployments ensure highly productive teams a path to continuous delivery.

The industry has seen significant changes to the way we use and store data over the past few years. Agile development practices have lead to greater emphasis on evolutionary database design, requiring new tools that support migration of schemas in line with changes to the domain model of an application. As storage space consistently becomes cheaper and data access speeds increase, many organizations are investigating the use of multiple schemas to hold data for different purposes, e.g. transactional and analysis schemas. Incremental data warehousing is becoming increasingly popular as the cost of moving data between a transactional data store and an analysis environment is less than the value of having access to near real-time reporting of critical business data.