Start with one workload and a clear decision. Build evidence with your data, move in manageable stages, and give your team the documentation and operating knowledge to take ownership.
A focused review may stop after discovery. A broader engagement continues only through the stages included in its agreed scope.
Agree the problem, baseline, business outcome, and dependencies.
Choose the data model, ingestion path, deployment, and controls.
Test correctness, speed, freshness, concurrency, and cost.
Implement, reconcile, and roll out in controlled stages.
Hand over alerts, runbooks, recovery procedures, and ownership.
Choose the workload and record how it performs today. Agree who will use the result, what must improve, and which constraints are fixed.
Prepare the target architecture and explain the important choices. Plan for the actual source data, query patterns, security boundaries, and people who will run it.
Use representative data distribution, concurrent queries, and ongoing ingestion. Check correctness, late arrivals, replay, failure recovery, and the resources needed to meet the target.
Build the agreed scope. For a migration, reconcile data and query outputs, run consumers in parallel where appropriate, and switch them in stages with rollback criteria.
Walk through normal operation, likely failure modes, recovery, upgrades, and cost review. Assign responsibility for the service and the pipelines around it.
Good preparation keeps the engagement focused. The scope identifies dependencies so data access and approvals do not become hidden delays.
Someone who can define the outcome and decide whether the result is useful.
Someone familiar with the source systems, data definitions, query consumers, and deployment process.
Representative queries and data, plus an agreed environment and access model. Use sanitized samples when appropriate.
Bring the uncertainty holding up your analytics decision. The first step is to define the evidence that would resolve it.