Infrastructure & OS
Auto-scaling, spot fleets, container migration, serverless where it fits. OS refresh from legacy Windows and Linux to modern, supported versions.
- EKS
- ECS
- Lambda
- Auto-scaling
Cloud · Modernization
Beyond lift-and-shift. Beyond the go-live.
Applications, data and databases: refactored for cloud, not just moved to it.
Most estates hit the cloud lifted-and-shifted: the fastest way in, and the right first step. Modernization is what turns that landing into actual cloud advantage.
Phoenix modernizes four layers: infrastructure and OS, applications, data platforms and databases. We identify what earns its keep as-is and what should be re-shaped, and we sequence the work so the business feels the benefit at each step, not just at the end.
What's in scope.
Application modernization
Refactor to cloud-native architectures: microservices, event-driven patterns, managed integration. Not for its own sake; for the specific outcomes the business asked for.
- Microservices
- EventBridge
- Step Functions
Data modernization
Data lakes and lakehouses on AWS: Redshift, S3, Lake Formation, Glue. SAP data sources integrated where the estate needs them.
- Redshift
- S3
- Glue
- Lake Formation
Database modernization
Oracle to Aurora PostgreSQL, SQL Server to Aurora MySQL, self-managed to managed. Migration + refactor combined where the licensing model demands it.
- Aurora
- PostgreSQL
- DMS
- SCT
The approach.
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01
Baseline
What's actually running and what it costs: usage patterns, dependencies, licensing, support obligations.
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02
Prioritize
Modernization backlog scored by business value, effort and blast radius. Sequenced with the customer.
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03
Deliver
Iterative delivery: smallest safe unit of value first, feedback loop tight, results reported.
Named deliverables.
Every engagement lands specific artefacts, not slides.
- Modernization baseline and prioritized backlog
- Reference architecture for target patterns
- Delivery of prioritized items, measured against baseline
- Runbooks and training for the modernized landscape
The wider cloud practice.
Landing Zone setup & deployment
Foundations that hold: production-ready from day one.
Migration
Wave-based migration: MAP-funded on AWS, minimal disruption to run-the-business operations.
Optimization & FinOps
Right-size the run. Own the spend.
Managed services
24/7 run under SLA: two tracks, one run team.
Frequently asked
Should we modernize during migration or after?
Both patterns work. 'Migrate then modernize' de-risks the move and unlocks cloud-native investment on a stable base; 'modernize on migration' makes sense when the target-state architecture is well-defined and the workload is a strong fit for containers or serverless. The decision is workload-by-workload.
Containers or serverless: how do you decide?
Serverless (Lambda, Step Functions, API Gateway) where workloads are event-driven, spikey or genuinely stateless. Containers (ECS or EKS) where you need long-running processes, close control over the runtime, or portability across environments. Real workloads often mix both under the same landing zone.
What about the database layer?
Database modernization is a first-class track: moving classic RDBMS onto managed services (Amazon RDS, Aurora), refactoring toward purpose-built engines (DynamoDB for key/value, Amazon Timestream for time series, OpenSearch for search), and archiving cold data to Amazon S3 tiered storage.
Do you re-platform SAP workloads?
SAP's own modernization path is the S/4HANA conversion (Brownfield) or Greenfield rebuild; Phoenix delivers both. Cloud-native modernization of SAP-adjacent workloads (custom Java, .NET, batch, integrations) sits within this practice.
How is business risk managed during modernization?
Strangler-fig migrations wherever possible: new components run alongside legacy under traffic split, until confidence is established. Well-Architected reviews at defined checkpoints, and rollback plans held for every deployment window.
The earliest conversations are usually the most useful.
Whether you're scoping an SAP move to cloud, restarting a stalled programme, or just trying to figure out where data and AI fit, start with a conversation.
