WorkloadSAP ECC
Purpose
Core ERP system for business operations
Environment
Windows / Unix
Cloud Platforms
AWS + Azure
Approach
Lift & Shift
Use Case
Cloud & Infrastructure / Use Case
Revolutionizing SAP Workloads Through Multi-Cloud Transformation
Facing growing requirements for agility, scalability, and cost efficiency, a leading multinational corporation partnered with SAI Technologies to transform its SAP workload management.
The transition moved an on-premises SAP landscape into a multi-cloud environment across AWS and Azure, supported by a standardized framework, toolchain, and expert guidance.
Transformation at a Glance
Transformation at a Glance
- Workload
- SAP
- Source
- On-Premises
- Target
- AWS + Azure
- Automation
- Terraform + Ansible
- Approach
- Phased Multi-Cloud Migration
The Challenge
The Challenge
The organization needed to improve agility, scalability, and cost efficiency while transitioning an established on-premises SAP landscape.
The migration required careful handling of legacy SAP ECC dependencies, critical custom applications, real-time data replication, workload sequencing, and validation with minimal disruption.
Cloud Transformation Strategy
A Phased Multi-Cloud Approach
Assess
Complete a comprehensive assessment of the existing SAP cloud and application landscape.
Prioritize
Prioritize workloads based on criticality and the requirements of each SAP component, data store, server, and application stack.
Place
Use AWS for complex workloads and Azure for database operations according to the supplied multi-cloud strategy.
Secure
Protect the SAP cloud tenant and business data through the environment controls described in the source: ISO standards, SOC certifications, and continuous monitoring.
Migrate & Validate
Synchronize real-time data replication and apply robust validation mechanisms to support a controlled transition with minimal disruption.
Workload Details
Moving the SAP Landscape
The SAP ECC transition required meticulous handling of legacy dependencies. AWS high-performance compute capabilities supported downtime minimization and zero data loss, while Azure database solutions streamlined migration of critical custom applications.
WorkloadClient/Server Applications
Purpose
Custom applications tailored for business needs
Environment
Unix / Windows
Cloud Platforms
AWS + Azure
Approach
Lift & Shift
WorkloadUnix Workstations
Purpose
Specialized systems for various tasks
Environment
Unix
Cloud Platforms
AWS
Approach
Lift & Shift
How It Happened
Automation, Replication, and Validation
Terraform and Ansible automated provisioning across AWS and Azure. During testing phases, this approach reduced deployment time by 60% and enabled faster iterations.
The migration required real-time data replication across continents. Robust validation mechanisms supported a seamless transition with minimal disruption.
Migration Decision Framework
Selecting a Migration Method
Migration-method selection considered the time available for data transfers, data volume, network speed, the number of repetitive transfer steps, and variance as those steps were repeated.
These factors were evaluated for each workload, including each data store, server, and application stack, before reaching consensus on the migration method.
Available transfer timeData volumeNetwork speedRepetitive transfer stepsVariance between repeated steps
- Assess Factors
- Evaluate Workload
- Select Migration Method
- Validate
- Migrate
Architectural Snapshot
A Simplified Multi-Cloud View
The transformation connected the on-premises SAP landscape to AWS and Azure, with Terraform and Ansible supporting provisioning and automation across the target environment.
On-Premises SAP↓Multi-Cloud Transformation↓
AWSAzure
↓SAP / Application Workloads↓Terraform + Ansible / Automation & OperationsConclusion
A Foundation for Future Innovation
The multi-cloud transformation delivered immediate operational efficiencies and established a scalable foundation for future innovation.
The next phase described in the source focuses on advanced modernization use cases, including AI-driven analytics and serverless architectures.
Results & Benefits
Results From This Use Case
These figures describe the outcome supplied for this specific multi-cloud transformation.
Uptime
Redundant architecture across AWS and Azure ensured near-zero downtime in this use case.
Process Efficiency Increase
Process efficiency increased across lines of business after migration, with improved response time.
Cost Savings
Optimized resource allocation significantly reduced operational and overall costs.
Agility
Automated deployment pipelines allowed quicker scaling during peak demands.
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