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AWS Mainframe Emulator (Benefits/Challenges (5 Steps to Migrating a…

- - - - Data is replicated to AWS (one way is to use messaging middleware)
      - Once replicated that data can move to an Amazon S3 data lake or Amazon Kinesis analytics
      - This prompts a move over to a data warehouse, dynomoDB or Aurora
      - Processing can be done with a data pipeline
      - Provides the ability to do real-time analytics and forecasting
    - - Create new processing and develop new functionally with the data
      - Integrates with APIs and devices. Mobile API or Alexa functionality to use data.
      - Seen as a different way to compute the data and expose it to consumers.
    - - The products and the environments will stay on the mainframe
      - All the Dev and test will happen on AWS (use IDE onto AWS and start coding there)
      - Put the code, commit the code and promote the code through the various environments within AWS
      - Use a mainframe emulator just for dev and test environment
      - Once the code has been promoted, then the code is pushed back to the mainframe for the final validation after it is executed into production.
  - - - Code along with complete stack (dependencies and middleware) are refactored.
      - automated reserve-engineering, entire stack is migrated over to AWS
      - Deep analysis of the behaviour of the program (i.e. Application model) to decide how transformation will happen
      - Automated forward engineering, find functional equivalent between target and source mainframe
      - Deploy mainframe to AWS
    - - The emulator will run code for you on AWS
      - Applications can horizontally scale or use elastic load balances on AWS
      - Medium size projects take on average one year
      - Code is converted or recompiled to work with AWS
    - - Legacy Hardware Emulation: Hardware emulator replaces the legacy hardware but the legacy OS and applications remain
      - Legacy Middle Emulation: Replaces legacy middleware APIs and OS APIs which allows porting. Source codes is recompiled.
      - Legacy Automated Refactoring: Code, data and dependencies are automatically converted to a modern language, data store and frameworks
      - Modern Middleware Platforming: Allows reuse of the application code and databases. Applies only to modern languages, middleware, and runtimes
- - - - Complex proof of concept
      - Maximum automation
      - Modernize Legacy data stores
      - Workload-based modernization
      - Define tool-evaluation factors
    - - Vendor-neutral pattern selection
      - Decide pattern, tool, architecture and activities
      - Serialize technical, then business-level modernizations
      - Estimate modernization and runtime costs
      - System integrators selection
- - - - Analysis of applications, languages, databases, networks, platforms and processes of the environment
    - - Design and architect the solution
    - - Apply migration rules to source code, review errors, find a fix, update migration rules, run the program through engine
    - - Perform load and stress tests to ensure your applications are prepared to handle high volumes
    - - Once applications are tested, verified, optimized then deployment can begin