Lift and Shift

Summary

Customer decided to make the move to the public cloud to be more flexible, scalable and have more control over costs. Customer wanted to be able to make use big data platforms, machine learning and AI.

The Challenge

Due to very strict timelines we had to work quickly in order to reach the deadlines of migrating to Amazon Web Services. Every delay would bring extra costs by extending their current contracts, also the duration of the lift and shift operation would mean extra costs by running their infrastructure in parallel. Large parts of their application landscape was not 'cloud ready' yet and we needed to find ways to properly deal with that without making compromises to availabilty and performance.

The Solution

By writing out their infrastructure as code that was kept in source control we made it possible to work simultaneously and even made their development team able to collaborate. Writing infra-as-code made enabled us to re-use large parts within the different stages of DTAP. Next to the inra-as-code we used configuration management tooling and software to automate the creation of 'golden machine images'. By using this combination we got the ability to make the infrastructure push button deployable which saved a lot of time and made deploying > 100 ec2 instances a breeze. The architecture was monitored with DataDog and logscraping was done with an ELK stack. From this point we were able to transform legacy applications into cloud native solutions. Lowering cost and increasing the cycle team of their development teams.