Databricks migration services Australia
Databricks migration and modernisation — Australia
A migration succeeds when the business can rely on the new workload and safely retire the old one. Copying tables is only part of the task. Transformations, schedules, access rules and downstream dependencies all affect the cutover decision.
FI Digital plans migration as a sequence of testable workload moves. The scope starts with source assessment and acceptance criteria, then addresses refactoring, reconciliation, performance and operational ownership.
What the engagement covers
Migration inventory
Identify sources, jobs, reports, dependencies and business-critical periods.
Pilot and refactor
Move a representative workload and test the assumptions that carry the most risk.
Parallel validation
Compare outputs, access behaviour, performance and cost against agreed baselines. Investigate differences rather than masking them.
Cutover and rollback
Define the final synchronisation, acceptance owner, fallback trigger and retirement conditions for the old workload.
Step 1 of 5 1. Migration inventory
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A practical example
In an illustrative month-end reporting migration, both environments run against the same agreed source period. Differences are explained before the new reports become authoritative. The old process remains available until the acceptance owner approves the transition.
Questions that shape the scope
Use these questions to identify the first decision and the information needed to make it.
- What must reconcile?
- Which jobs depend on the workload?
- What triggers rollback?
- When can the old environment be retired?
Plan the Australian operating handover
Identify the Australian process owner, the systems already in use and any group teams that will approve or operate the change. Where customers, staff or data cross regions, document the actual access and support arrangements. The delivery plan should fit the working hours, reporting calendar and decision responsibilities of the team that will use it.
From assessment to an accepted result
Define the decision and sources
Agree the business measures, authoritative records, freshness and access requirements.
Build a representative path
Connect and transform a bounded dataset, making validation and failure handling visible.
Reconcile and test access
Compare business results with accepted sources and check permissions across the actual delivery path.
Operate with clear ownership
Document monitoring, recovery, changes and cost review for the nominated technical and business owners.
Control cost around the work that matters
Control cost around the actual workload and the people who will operate it. More frequent refresh, additional history and a larger platform can each introduce recurring effort that is not visible in the initial build estimate. Test a representative path before scaling every source. Review compute, storage, licences, monitoring and support separately, and retain existing components that meet the requirement. Reusable tooling can accelerate engineering, but it cannot settle conflicting definitions or approve an unexplained difference in reported figures. Compare the cost of an accepted, maintainable information flow rather than the price of connecting a source alone.
What your team should receive at handover
The handover should describe source ownership, transformations, important definitions and the route from a failed load to a corrected result. Record reconciliation evidence, access decisions and the assumptions behind freshness or capacity. Include the changes that require business approval, rather than leaving every decision with a technical operator. The nominated owners should understand how to investigate a discrepancy and when to stop distribution of an unreliable result.
Scope and responsibilities
The scope, acceptance criteria, third-party dependencies and operating responsibilities are agreed for the engagement. No partner tier, certification, guaranteed saving or fixed delivery period is implied.
Frequently asked questions
Can we migrate everything in one release?
That may be possible for a small, well-understood scope, but it should not be the default assumption. Group workloads by dependencies and business risk. A representative pilot can expose data, performance and access issues before a broader cutover, while a phased plan preserves a clearer route to investigation and rollback.
How do you compare cost before and after migration?
Use representative workload runs and include the full operating picture: compute, storage, licences, engineering and support effort. Compare equivalent outputs and service expectations. A short test or a discounted environment can be misleading, so record assumptions and validate them before treating a projected saving as a realised result.
Should we change platform before fixing the data?
A platform change does not resolve inconsistent definitions, missing ownership or unreliable source records on its own. Assess the current problem and the intended workloads first. Some organisations need a better pipeline or operating process; others need a different architecture. The recommendation should connect each proposed change to an observed limitation and an agreed acceptance measure.
How do you estimate data engineering work?
The estimate depends on sources, access methods, transformations, history, freshness, governance and operational expectations. Representative samples help expose complexity that a list of system names cannot show. Separate initial delivery from recurring compute, licences and support. A bounded discovery or representative workload can establish a more useful scope before a wider implementation commitment.
How will we know the result is correct?
Agree reconciliation rules with the people who own the business meaning. Compare representative records, totals and exceptional cases against authoritative sources, and document acceptable differences. Technical success signals are useful but insufficient: a job can finish successfully while applying the wrong definition. Acceptance should cover both processing behaviour and the decisions the information is intended to support.
How do permissions carry through the solution?
Map who may view, change and approve information across sources, processing, models and reports. Test the actual access paths, including service accounts, exports and derived datasets. A permission applied in one layer does not automatically protect every copy. The design should make ownership and review responsibilities clear without assuming that a platform setting provides complete governance.
Can our internal team run the solution afterwards?
The handover should match your team’s responsibilities and skills. Include source and transformation documentation, access ownership, failure investigation, recovery and change procedures. Where ongoing support is required, agree its scope and service expectations separately. A useful acceptance exercise is to have the nominated operator investigate a representative failure using the supplied documentation and agreed access.
What information is needed for the first discussion?
Describe the business question, source systems, current refresh process and the decisions delayed by poor information. Approximate scale and known exceptions help, but confidential datasets are not needed in a website enquiry. Start with a synthetic or approved sample and agree secure access only after the purpose, scope and responsible owners are clear.
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