Migration discovery & target design
Inventory warehouse structures, transformations, reports, semantic models and the business processes that depend on them.
Fabric migration, Power BI and capacity operations
Fabric Migration & Power BI Optimisation
Move data and reporting to Microsoft Fabric without losing business meaning. Then improve the Power BI estate and operate capacity around measured demand.
A migration is finished when the business can trust and run the new system.
Moving tables is only part of the job. Measures, reports, permissions, refreshes and support arrangements also have to survive the move. We validate the old and new paths side by side before cutover.
More than 10 migrations delivered to Microsoft Fabric, mainly from Snowflake, Azure Synapse, Azure SQL and on-premises SQL Server.
Core service capabilities
Microsoft Fabric, Power BI & capacity operations
Fabric migration & controlled cutover
Move data and reporting in manageable stages, reconcile the new path and agree acceptance, rollback and handover.
Power BI estate optimisation
Improve tenant and workspace design, semantic models, DAX, report behaviour and refresh reliability without separating performance from governance.
Fabric capacity optimisation
Connect utilisation, workload windows and service constraints to capacity sizing, scheduling, autoscaling policy and cost scenarios.
Workload & refresh tuning
Investigate queries, processing patterns, job overlap and refresh design before treating more capacity as the answer.
Monitoring & operating review
Track why performance or capacity changed, observe the result and compare the estate with the agreed baseline.
Microsoft Fabric work, with the scope kept visible
Selected engagement evidence
Redshift, Tableau & Kleene.ai to Fabric
A UK mortgage lender brought data preparation, warehousing and reporting responsibilities into Microsoft Fabric. The approximately AUD 75,000 annual platform and licensing saving is client-reported; the original total cost and vendor split were not supplied.
SQL Server modelling for Fabric
Metadata and data profiling informed proposed fact, dimension, Silver and Gold structures, with people retaining responsibility for grain, relationships and measures. Approximately AUD 40,000 in legacy licence cost was reported as avoided; the cost period was not supplied.
Capacity matched to changing demand
Telemetry, workload windows and service checks informed a responsive capacity policy. Client-supplied figures put the monthly run rate at approximately AUD 14,000 before and AUD 3,000 after; these are engagement figures, not a universal Fabric benchmark.
Data-platform modernisation and governance
Related work across Fabric administration, migration, asset-data governance, real-time ingestion and Power BI reporting for an Australian electricity network. Outcome reported as delivered scope.
How an engagement moves from questions to an operable platform
Discovery, delivery and handover
Establish the workload case
Map sources, transformations, semantic models, reports, identities, schedules and the business cycles that cannot be disrupted. The assessment can conclude that some workloads should stay where they are.
Design the target and the controls
Agree data layers, workspace boundaries, ownership, security, deployment paths and capacity assumptions before rebuilding the estate.
Build and reconcile in stages
Keep the existing path available while records, balances, measures, report behaviour and access are compared against agreed acceptance checks.
Cut over, hand over and observe
Agree rollback and support ownership, then tune refreshes, queries and capacity using evidence from real operation rather than design-time assumptions.
Explore the approach before we talk
Architecture, guidance and demonstration
Microsoft Fabric Architecture & Data Engineering
See how source ownership, ingestion, validation, OneLake data layers and reporting readiness fit into one governed path.
Fabric capacity demonstration
Explore a browser-based capacity policy using a synthetic workload example. Supplied engagement figures are identified separately from the demonstration data.
Fabric migration readiness guide
Work through the workload, controls, capacity, data-quality and cutover questions that should be answered before migration begins.
Fabric or Synapse?
Compare the operating questions behind the platform decision rather than treating migration as a feature checklist.
Process → Architecture → Delivery
Our methodology
- 01DiscoverAgree the outcome, the decisions and the requirements.
- 02Current stateMap how the work and data flow today, with evidence.
- 03Root causesFind what drives the delay, rework or disagreeing numbers.
- 04ApproachAgree the target design, scope and measures of success.
- 05BuildBuild the data model and reports on the agreed design.
- 06OptimiseTest at real volumes; tune speed, cost and usability.
- 07ProductioniseRelease with managed deployment, monitoring and support.
- 08GovernSet owners, access and controls so it keeps working.
Process redesignTo-be: per-workload cutover with acceptance and rollback Cutover is a governed decision: every check type passes, business acceptance is recorded, and a rollback owner and window are agreed before switching consumers.
Steps in this diagram
Lanes: Migration team, Data owner, Business acceptance, Steering / CAB.
- Workload rebuilt
- Parallel run + reconcile
- All checks pass?
- Explain / fix variance
- Acceptance test: reports + access
- Approve cutover + rollback owner
- Switch consumers, freeze legacy
- Hypercare window
- Legacy retired
Reference architectureStaged migration with a parallel run and reconciliation The legacy path stays live while Fabric is built and reconciled in stages. Cutover is per workload, with rollback ownership agreed. Capacity Metrics and workspace monitoring then feed the operate-and-optimise loop.
Components in this design
- Azure Synapse (dedicated SQL pool)
- SQL Server (on-prem / Azure SQL)
- Snowflake (cloud warehouse)
- Mirroring (keep in sync in parallel)
- Copy job / pipelines (history + increments)
- Fabric warehouse (rebuilt model)
- New semantic model (Direct Lake)
- Legacy model (kept until cutover)
- Reconciliation (records, balances, measures)
- Readiness report (sample report on this page)
- Capacity Metrics (CU, throttling)
- Capacity policy (resize within bounds)
Delivery: interactive Power BI sample reportFabric Migration & Estate Operations Track migration waves and show, per workload, whether records, balances, measures, report behaviour and access match between the legacy and Fabric paths. Delivered with named owners, role-based access, release pipelines and a controlled way to change governed measures.
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Synthetic sample data. Click any bar, point or row to cross-filter; use the tabs at the bottom to change page.
Next step
Planning a move to Fabric, or a Power BI estate nobody trusts?
Tell us what is happening. We agree priorities and scope before proposing any work.