DevOps, Infrastructure-as-code (IAC), and other cloud native ideas are used to modernize legacy applications, improve performance, and respond to changing business speeds. To modernize legacy apps, data, and infrastructure, examine them and choose the proper method
Rebuild provides the most advantage but takes a lot of investment, while rehost moves programs and data to the cloud without optimization and requires less cost but less value. To adapt to technology and business changes, modernized applications are installed, monitored, and updated.
Many companies realize that cloud migration is not providing enough value or agility/speed beyond platform-level automation. Conway’s law states that IT organization affects how applications/services are designed and managed.
Discovery involves understanding historical applications, infrastructure, data, application-service-data interactions, and security. Planning divides the complicated portfolio of apps into iterations to be modernized and forms an iterative roadmap and implementation strategy.
Modernization approach determines blueprint/design phase activities. Build, test, and deploy follow. Let’s study Generative AI across various lifecycles.
A modernization program must balance concurrent and sequential efforts, and identify co-existence scenarios to create a macro plan. This is usually done once through Program Increments (PIs), but planning exercises with execution-level inputs is much harder.
It involves generating various code artifacts, such as IAC (Terraform or Cloud Formation Template), pipeline code/configurations, embedded security design points, application code generation from swaggers or legacy data, and firewall configurations.