Cloud analytical architecture
Choose storage, transformation and serving components according to freshness, concurrency and data-sharing requirements.
Cloud Analytics
Cloud Analytics
Move analytical workloads onto managed cloud services when elasticity, data sharing or operating effort is constraining reporting.
Inside the delivery
An analytical workload matched to cloud consumption
Identify query patterns, freshness targets, users and peak concurrency. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Demand mapping
Identify query patterns, freshness targets, users and peak concurrency.
Output
Analytical workload and usage profile
- 02
Platform design
Select managed storage and compute with access boundaries and cost assumptions.
Output
Cloud analytics architecture and cost model
- 03
Workload transition
Move selected transformations and reporting connections with reconciliation checks.
Output
Validated analytical workload configuration
- 04
Consumption tuning
Measure query behavior and tune scheduling, caching and capacity to the observed workload.
Output
Performance findings and consumption dashboard specification
Controls across the workflow
- Access boundaries
- Query monitoring
- Cost attribution
- Freshness checks
Decisions that shape the scope
- Will cloud hosting resolve conflicting business metrics?
- No. Hosting changes where analytical workloads run. Metric definitions and ownership require stakeholder decisions, often supported by analytics engineering or governance work.
- What needs to be available before delivery?
- Existing analytical workloads, source connections, query history, user concurrency and spending or residency constraints.
Before you commit
Is this the right engagement?
- What we need from you
- Reporting workloads, data sources, residency requirements, query patterns and a working cost baseline.
- How you accept the work
- Reconcile representative reports, test access controls and measure query performance and consumption costs under an agreed workload.
- Scope & alternatives
- No. Hosting changes where analytical workloads run. Metric definitions and ownership require stakeholder decisions, often supported by analytics engineering or governance work.
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Workload migration
Move a representative dataset and report set with reconciliation against the existing source of record.
Consumption controls
Set workload isolation, monitoring and cost attribution so analytical usage can be reviewed by the responsible team.
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Microsoft Azure
AWS
Databricks
Snowflake
dbt
Power BI and Tableau
Frequently asked questions
Questions to resolve before starting
01Will cloud hosting resolve conflicting business metrics?
Will cloud hosting resolve conflicting business metrics?
No. Hosting changes where analytical workloads run. Metric definitions and ownership require stakeholder decisions, often supported by analytics engineering or governance work.
02What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Existing analytical workloads, source connections, query history, user concurrency and spending or residency constraints.
03What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes performance findings and consumption dashboard specification. Measure query behavior and tune scheduling, caching and capacity to the observed workload.
04How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Existing analytical workloads, source connections, query history, user concurrency and spending or residency constraints. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Related
Related services and delivery options
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for Cloud Analytics.