Business Challenge
Moving established data products toward Azure without increasing migration risk
The customer had an established AWS Databricks platform supporting multiple business solutions and data products. The target state required an Azure transition while maintaining existing functionality, preserving downstream analytics, and creating a practical path for additional workloads.
The environment included ADLS landing zones, REST API ingestion, Bronze, Silver, and Gold processing layers, internal automation, metadata-driven ETL frameworks, and Fabric-based analytics consumption.
Factory Approach
A ring-fenced implementation that became a repeatable pattern
The delivery team focused on a clearly defined migration sprint and representative business workload. The objective was to produce a working implementation together with architecture guidance and knowledge transfer, rather than a standalone technology evaluation.
Environment assessment
Review ingestion, APIs, jobs, notebooks, storage, catalog structures, runtimes, and deployment dependencies.
Target architecture
Define source-to-target mapping, environment strategy, deployment assumptions, security boundaries, and validation approach.
Migration implementation
Establish a methodology for workload migration, reconciliation, deployment, and operational handoff.
Knowledge transfer
Document decisions, procedures, operational considerations, and scale-out recommendations for future teams.
Migration Pattern
Preserving downstream analytics while creating a path to scale
The migration pattern accounted for the full workload lifecycle: ingestion, transformation, orchestration, deployment, validation, and downstream analytics consumption.
Source assessment
Identify workload, notebook, job, and data dependencies before development.
Validation framework
Compare migrated outputs and downstream behavior against the existing implementation.
Deployment guidance
Define environment promotion, access assumptions, and operational handoff practices.
Scale-out playbook
Reuse the established pattern for future workloads with less discovery and delivery risk.
Business Impact
A migration blueprint for broader Azure adoption
Reduced uncertainty
A working reference pattern clarified migration dependencies and delivery assumptions.
Faster onboarding
Future workloads can reuse the assessment, implementation, and validation approach.
Documented standards
Architecture decisions, deployment guidance, and operational practices were captured for reuse.
Lower future risk
A bounded implementation reduced the risk of applying the pattern at broader scale without evidence.
Preserved analytics
The approach maintained downstream Fabric analytics and reporting considerations.
Knowledge transfer
Customer teams and delivery partners gained guidance for subsequent migration waves.
Customer and delivery-partner names are not disclosed. The case study describes the migration pattern, documented outputs, and reported business impact supplied for this engagement.