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.
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.
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.
The Factory succesfully migrated many workloads from on-premise to public cloud. We can advise and support you defining your cloud strategy. Whether this is a cloud-first approach, or you want to have a hybrid setup. Running the right workloads in the right cloud is essential. Our experts can assist with defining strategy, designing your future operating model, migration and to run this efficient, secure and cost aware.Read More