Discovery and workload inventory
Map pipelines, tables, schedules, dependencies, service levels, and operational owners before movement begins.
Service 02
Migration, Modernisation & Performance
Move critical workloads with a clear inventory, controlled validation, measurable performance, and an operable target environment.
Good fit when
An ageing platform has become expensive or difficult to support.
Migration dependencies and validation effort are unclear.
Jobs run too slowly, fail unpredictably, or consume excessive resources.
Delivery scope
What the engagement can cover.
Target design and conversion
Translate established workloads into maintainable target-platform patterns without blindly reproducing old constraints.
Validation and reconciliation
Define expected results, automate comparisons, and make discrepancies visible before cutover.
Performance engineering
Profile bottlenecks, redesign jobs, tune orchestration, and benchmark improvement under representative loads.
Cutover and production readiness
Plan migration waves, rollback, support, monitoring, documentation, and ownership for go-live.
Designed around outcomes
What should be different when the work is done.
- A migration plan grounded in real workload dependencies
- Reduced cutover risk through repeatable validation
- Faster and more stable workloads on the target platform
- Documentation and handover that operations teams can use
Related delivery
Modernising a large-scale data platform for production migration
A controlled migration of hundreds of ETL workloads and enterprise data assets from a legacy data lake to HPE Data Fabric Software.
Read the case study500+ETL workloads
500+tables
100 TBdata estate
Service FAQs
Questions teams ask before starting.
Direct answers based on how these engagements are scoped and delivered.
How does a data-platform migration usually begin?
It begins with evidence: an inventory of workloads, schedules, data sets, dependencies, service levels, credentials, owners, and downstream consumers. That inventory shapes migration waves, estimates, validation, cutover, and rollback.
How do you prove that migrated data is correct?
Monic defines reconciliation before conversion. Depending on the workload, evidence can include row counts, aggregates, business-rule outputs, exception records, source-to-target comparisons, and representative edge cases.
Can migration and performance optimisation happen together?
Yes. A direct conversion may preserve the bottlenecks of the old platform. Representative benchmarks help identify when a workload should be redesigned, tuned, or re-orchestrated as part of the move.
How do you reduce cutover risk?
The plan can use migration waves, parallel operation, automated validation, clear go or no-go criteria, rollback triggers, production monitoring, and named decision owners rather than relying on a single high-risk switch.
Start with the work
Bring us the operational problem.
We will help determine whether the right next step is data engineering, migration, automation, or a focused AI pilot.