Migrating an on-premises private cloud to AWS. We started from the design and ended up powering down the on-premises platform, with the new environment ready for big data and managed services.
End-to-end migration from on-premises private cloud to public cloud.
Migrated to AWS for elastic capacity and a global footprint.
All infrastructure defined as code, with version control and reuse across environments.
Capacity adjusts on demand; resources are allocated to fit the workload.
Pay-per-use and right-sizing replaced fixed on-premises capacity costs.
Big data, machine learning, and AI services available on day one.
The customer decided to move to public cloud for capacity flexibility, cost control, and access to big data, machine learning, and AI services.
The timeline was strict. Every week of delay extended on-premises contracts and the cost of running both environments in parallel. A large part of the application landscape was not cloud-ready, and we had to handle that without compromising availability or performance.
Writing the infrastructure as code, kept in source control, let multiple engineers work in parallel and brought the development team into the same workflow. The same code was reused across DTAP stages.
Configuration management and image-build automation produced golden machine images. Combined with Infrastructure as Code, the platform was push-button deployable, which made rolling out more than 100 EC2 instances straightforward.
The architecture was monitored with Datadog; logs were collected through an ELK stack. From there, we modernised legacy applications into cloud-native services, which lowered costs and shortened the development cycle.
The Factory has migrated many workloads from on-premises to public cloud. We help define cloud strategy: cloud-first or hybrid: and run the right workloads in the right cloud. Our architects help with strategy, target operating model, migration, and day-two operations: secure and cost-aware.
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