Assessment and architecture
Clarify use cases, assess the current estate, compare Azure service options, define guardrails and create a prioritised target-state roadmap.
Cloud Data Platforms Service
Build a Governed Microsoft Azure Data Platform for Scale
Dataconsultant helps organisations assess, design, migrate, implement and operate Microsoft Azure data platforms. We align Azure services, data architecture, governance, security, delivery practices and cost controls with the business decisions and analytics workloads the platform must support.
- Business-led Azure architecture and workload planning
- Security, privacy and governance built into delivery
- Migration, engineering and operational transition support
- Documentation, testing and knowledge transfer included
Illustrative architecture
Design referenceAzure data platform operating view
Business and operational sources
ERP and finance
CRM and customer
Applications and APIs
Ingestion and integration
Azure Data Factory
Event Hubs
API and file transfer
Lakehouse, processing and serving
ADLS Gen2
Databricks / Fabric
Synapse / SQL
Consumption and decisions
Power BI
Data products
AI and applications
Purview
Entra ID
Key Vault
Azure Monitor
Quick definition
What is a Microsoft Azure data platform service?
A Microsoft Azure data platform service combines advisory, architecture, engineering, migration, governance, assurance and operational support to create a reliable cloud environment for enterprise data. It may cover batch and streaming ingestion, lakehouse or warehouse design, transformation, data quality, metadata, analytics, security, cost management and managed operations.
The goal is not simply to deploy Azure products. It is to establish a platform that teams can govern, use, support and improve against defined business outcomes.
Service offering
Azure platform support from decision to operation
Scope can be shaped around a focused technical need or an end-to-end platform programme.
Engineering and migration
Implement ingestion, storage, transformation, orchestration, modelling, analytics, infrastructure automation and migration controls.
Assurance and managed support
Establish monitoring, runbooks, data-quality controls, access reviews, cost reporting, release support and continuous improvement.
Key value propositions
Practical value from a well-designed Azure data foundation
A platform should improve delivery and control without creating unnecessary product complexity.
Trusted analytics
Improve consistency, traceability and confidence by applying shared data models, quality rules, lineage and governed access.
Faster onboarding
Use repeatable ingestion patterns, automated deployment and reusable components to reduce effort when adding data sources and use cases.
Controlled cloud cost
Match services and capacity to workload needs, define ownership and monitor consumption before cost becomes difficult to explain.
Operational resilience
Design for monitoring, recovery, supportability, release control and clear accountability across business and technology teams.
Problems addressed
Common Azure data platform challenges we help resolve
Fragmented data tools and duplicated pipelines
Response: Rationalise services, define standard patterns and create an architecture decision record that explains when each Azure capability should be used.
Slow, unreliable or difficult-to-support data delivery
Response: Introduce deployment automation, observability, data contracts, testing, runbooks and ownership for production pipelines.
Unclear governance, lineage and access controls
Response: Connect Purview, identity, classification, policy, stewardship and control evidence to the platform operating model.
Cloud spending without workload transparency
Response: Establish tagging, capacity choices, cost allocation, performance baselines and workload-level reporting.
Need an independent view of your Azure data estate?
Start with a focused assessment of architecture, delivery risks, governance, cost and operational readiness.
Suitability
Who this service is for
The service supports organisations planning a new Azure capability, improving an existing platform or moving data workloads from legacy environments.
Good fit
- You need a business-aligned Azure data architecture and delivery roadmap.
- You are migrating data warehouses, lakes, ETL workloads or analytics to Azure.
- You need governed self-service analytics, a lakehouse or reusable data products.
- You need stronger reliability, security, lineage, quality or cost control.
- You require specialist capacity alongside internal cloud and data teams.
- You want implementation support followed by structured operational transition.
May not be the right fit
- You only need a standard Microsoft 365 configuration task unrelated to data platforms.
- A single SaaS reporting tool fully meets a simple and stable requirement.
- You need a licensed legal opinion, statutory audit or formal certification.
- You cannot provide accountable stakeholders, source-system access or security decisions.
- You require unsupported customisations that conflict with Microsoft platform guidance.
- A permanent internal hire is more suitable than a defined external engagement.
Common use cases
Azure data platform scenarios
1
Legacy warehouse modernisation
Move on-premises warehouse and ETL workloads to an Azure warehouse or lakehouse while managing dependencies, reconciliation and cutover risk.
2
Enterprise lakehouse foundation
Create governed storage, processing, semantic and serving patterns for analytics, data science and reusable domain data products.
3
Customer and operational analytics
Integrate CRM, digital, transaction and service data to support trusted reporting, segmentation and operational decisions.
4
Real-time event processing
Design event ingestion and low-latency processing for telemetry, fraud indicators, logistics, digital interactions or operational alerts.
5
Governed Power BI enablement
Provide curated data, semantic models, ownership and deployment controls for scalable reporting and self-service analytics.
6
AI-ready data foundation
Improve access, quality, documentation and control of enterprise data used by machine learning, generative AI and intelligent applications.
Capabilities
Microsoft Azure data platform capabilities
Strategy and architecture
Workload assessment, platform option analysis, target architecture, landing-zone requirements, integration patterns, non-functional requirements and roadmap planning.
Data engineering
Batch and streaming ingestion, transformation, orchestration, data modelling, reusable frameworks, CI/CD, infrastructure-as-code and performance optimisation.
Migration and modernisation
Discovery, dependency analysis, migration waves, conversion, reconciliation, parallel runs, cutover planning, rollback preparation and decommissioning support.
Governance and assurance
Metadata, lineage, classification, access design, data quality, retention, control mapping, test evidence, architecture review and production-readiness assurance.
Platform operations
Monitoring, incident response, service health, capacity management, cost reporting, release support, access reviews, runbooks and continual improvement.
Deliverables
Typical outputs from the engagement
Final deliverables depend on whether the engagement is advisory, implementation-led, migration-focused or managed.
| Work area | Typical deliverables | Decision or operational purpose |
|---|---|---|
| Assessment | Current-state findings, workload inventory, dependency map, risk register and maturity view | Establish evidence, constraints and priorities |
| Architecture | Target-state diagrams, architecture decisions, service-selection rationale and non-functional requirements | Guide design and prevent unplanned tool sprawl |
| Engineering | Ingestion and transformation pipelines, data models, reusable code, CI/CD and infrastructure definitions | Create repeatable and supportable delivery |
| Governance | Classification, lineage, access model, quality controls, retention mapping and control evidence | Support trust, accountability and compliance |
| Migration | Wave plan, reconciliation rules, test evidence, cutover plan and decommissioning backlog | Reduce migration and continuity risk |
| Operations | Monitoring design, dashboards, support model, runbooks, service catalogue and knowledge-transfer pack | Enable stable ownership after go-live |
Define the right deliverables before implementation begins
We can help translate business, technical and control requirements into a practical Azure delivery scope.
Service process
How Dataconsultant delivers Azure data platform work
The stages are adapted to the engagement; implementation proceeds through agreed controls rather than a fixed generic timeline.
Discover and align
Confirm business outcomes, stakeholders, workloads, constraints, dependencies and acceptance criteria.
Primary output: agreed scope and evidence planAssess the estate
Review sources, data flows, platforms, quality, security, governance, skills, costs and operational risks.
Primary output: current-state findings and risk registerDesign the target state
Select suitable Azure patterns, define architecture decisions, controls, environments and operating responsibilities.
Primary output: target architecture and delivery backlogBuild or migrate
Implement prioritised platform components, pipelines, models, automation, controls and migration waves.
Primary output: tested platform incrementsValidate and assure
Perform reconciliation, performance, resilience, security, data-quality and production-readiness checks.
Primary output: test evidence and acceptance recordTransition and improve
Complete runbooks, training, support handover, service metrics, cost reporting and improvement planning.
Primary output: operational transition packTechnology and frameworks
Azure services, standards and control references
Technology selection is based on workload and operating requirements. Not every engagement needs every service.
Ingestion and integration
- Azure Data Factory
- Event Hubs
- Logic Apps
- API Management
Storage and processing
- Azure Data Lake Storage
- Azure Databricks
- Microsoft Fabric
- Azure Synapse Analytics
Governance and security
- Microsoft Purview
- Microsoft Entra ID
- Azure Key Vault
- Defender for Cloud
Analytics and operations
- Power BI
- Azure Monitor
- Log Analytics
- Azure DevOps / GitHub
Relevant reference points
Applicable legal, regulatory, contractual and certification requirements must be confirmed for the organisation, sector and jurisdictions involved.
Unsure which Azure services fit your workloads?
An architecture and workload assessment can compare options before capacity and implementation commitments are made.
Engagement models
Ways to engage Dataconsultant
| Model | Suitable when | Typical scope | Client participation |
|---|---|---|---|
| Focused assessment | A decision, risk or architecture question must be resolved | Evidence review, workshops, findings and recommendations | Sponsor, architects, platform owners and security stakeholders |
| Defined implementation | A platform component or use case has an agreed outcome | Design, build, test, documentation and handover | Product owner, subject experts, environment and approval support |
| Migration programme support | Multiple workloads must move through controlled waves | Discovery, factory patterns, migration, assurance and cutover | Source owners, testers, business approvers and operations teams |
| Embedded specialists | Internal teams need additional architecture, engineering or governance capacity | Named roles working within client delivery governance | Backlog ownership, access, standards and day-to-day prioritisation |
| Managed platform support | Ongoing operation and improvement require specialist coverage | Monitoring, incidents, releases, controls, cost and service reporting | Service owner, escalation paths and agreed responsibilities |
Practical examples
Illustrative Azure data platform scenarios
These examples demonstrate how scope may be structured; they are not claims of completed client results.
Retail analytics modernisation
Situation: Sales, inventory and ecommerce reporting relies on disconnected extracts and overnight manual processing.
Possible service response: Build governed ingestion into ADLS, process data through Databricks or Fabric, publish curated models to Power BI and implement freshness, quality and cost monitoring.
Financial-services migration
Situation: A legacy warehouse has rising support costs, complex dependencies and strict control requirements.
Possible service response: Create a workload inventory, define migration waves, implement secure landing-zone patterns, reconcile critical reports and retain control evidence for review.
Manufacturing event platform
Situation: Operational teams need faster visibility of machine, production and quality events.
Possible service response: Design event ingestion, streaming processing, durable storage and operational dashboards with clear latency, resilience and support requirements.
Professional-services data foundation
Situation: Finance, project and resource information is inconsistent across business systems.
Possible service response: Establish shared definitions, source-to-target rules, governed semantic models and scheduled pipelines for management reporting and planning.
Outcomes and KPIs
How platform progress can be measured
Metrics should be tied to agreed baselines, workload priorities and accountable owners.
Pricing and cost factors
What influences Azure data platform service cost?
A written estimate should separate consulting effort from Microsoft Azure consumption, licences and third-party costs.
Scope and complexity
Number of sources, domains, workloads, environments, business units and integrations.
Data characteristics
Volume, velocity, history, quality, sensitivity, retention and latency requirements.
Migration risk
Legacy dependencies, reconciliation effort, downtime tolerance, cutover and rollback needs.
Controls and assurance
Security architecture, regulatory mapping, test evidence, reviews and documentation depth.
Engineering maturity
Existing automation, standards, reusable components, skills and environment readiness.
Delivery model
Fixed scope, team augmentation, migration programme, onsite needs or managed support.
Service choices
Compute, storage, orchestration, networking, monitoring and licensing decisions.
Operational coverage
Support hours, service levels, incident responsibilities and improvement backlog.
Get a scope-based estimate
Provide your objectives, current technologies, workload priorities and delivery constraints for an initial scoping discussion.
Why consider Dataconsultant
Specialist support across data, cloud and governance decisions
Dataconsultant approaches Azure as an enterprise data capability, not only a collection of cloud services.
Outcome-led scope
Architecture and engineering are linked to decisions, users, controls and measurable service expectations.
Evidence-conscious delivery
Assumptions, dependencies, limitations, tests and architecture decisions are documented.
Cross-functional perspective
Business, data, architecture, security, privacy, governance and operations requirements are considered together.
Transferable capability
Runbooks, documentation, training and transition planning help internal teams own the platform.
Security, quality, privacy and compliance
Control considerations built into platform delivery
Controls must be appropriate to the organisation’s data, sector, contracts and jurisdictions.
Security
- Identity, least privilege and privileged access
- Network segmentation and private connectivity
- Encryption, keys, secrets and certificate handling
- Logging, monitoring and incident evidence
Data quality
- Critical data elements and business rules
- Validation, reconciliation and exception workflows
- Ownership, thresholds and issue remediation
- Quality monitoring across pipeline stages
Privacy and residency
- Classification and handling requirements
- Purpose, minimisation, retention and deletion
- Regional deployment and cross-border considerations
- Masking, tokenisation and controlled access
Compliance and third-party risk
- Obligation and control mapping
- Supplier, licence and service dependencies
- Evidence retention and review responsibilities
- Legal, regulatory and audit escalation points
This service does not replace legal advice, formal certification, statutory audit or specialist penetration testing unless those services are separately commissioned from appropriately authorised providers.
Delivery environment
Technology ecosystems and operating context
Azure data platforms rarely operate in isolation. Delivery must account for connected applications, cloud controls and enterprise processes.
Microsoft ecosystem
Azure subscriptions, management groups, Entra ID, Microsoft 365, Dynamics 365, Power Platform, Fabric and Power BI.
Enterprise applications
SAP, Oracle, Salesforce, service platforms, ecommerce systems, databases, APIs, files and industry applications.
Engineering toolchain
Azure DevOps, GitHub, Terraform, Bicep, testing frameworks, package management and release controls.
Data and AI workloads
Business intelligence, data science, machine learning, generative AI, operational analytics and data products.
Control functions
Architecture, information security, privacy, risk, compliance, internal audit, records management and procurement.
Operating model
Platform teams, domain teams, product owners, data stewards, service management, FinOps and managed providers.
Customer perspectives
Representative feedback on Azure data platform support
The following testimonials are realistic, service-specific examples of the experience organisations may value. They are not presented as independently verified customer claims.
★★★★★
“The team helped us turn a broad cloud ambition into clear architecture decisions, delivery priorities and responsibilities. Communication was structured, technical trade-offs were explained in business terms, and the documentation gave our internal engineers a practical basis for implementation.”
★★★★★
“Our migration planning improved significantly once dependencies, reconciliation needs and cutover risks were made visible. The consultants handled revisions professionally and kept business owners involved rather than treating the programme as a purely technical exercise.”
★★★★★
“The Azure platform design balanced engineering speed with governance. We received useful patterns for ingestion, access, data quality and monitoring, along with clear guidance on where Databricks, Fabric and native Azure services were most appropriate.”
★★★★★
“Security and privacy requirements were addressed early, not added at the end. The team worked constructively with our cloud, risk and legal stakeholders, documented assumptions carefully and revised the design when residency and access constraints became clearer.”
★★★★★
“The delivery approach gave us working platform components as well as the operational material needed to support them. Runbooks, monitoring expectations, cost ownership and handover sessions were handled with the same care as the engineering work.”
★★★★★
“We needed specialist capacity without losing control of the roadmap. The consultants integrated well with our team, communicated delivery risks early, responded professionally to changing priorities and improved the consistency of our Azure data engineering practices.”
Frequently asked questions
Microsoft Azure data platform service FAQs
What is a Microsoft Azure data platform service?
It is a consulting and implementation service that helps an organisation design, build, migrate, govern and operate data capabilities on Microsoft Azure. Scope may include ingestion, storage, transformation, analytics, governance, security, observability, cost management and operating-model support.
Which Azure data services can be included?
Depending on requirements, the platform may use Azure Data Lake Storage, Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Microsoft Fabric, Microsoft Purview, Power BI, Event Hubs, Stream Analytics, Azure SQL, Cosmos DB, Key Vault, Monitor and related services.
Can Dataconsultant migrate an existing data warehouse to Azure?
Yes. Migration support can cover discovery, dependency mapping, target architecture, data and workload migration, reconciliation, performance testing, cutover planning and transition. The migration approach depends on source technologies, data volumes, downtime tolerance and regulatory constraints.
How do you choose between Synapse, Databricks and Microsoft Fabric?
The choice should be based on workload patterns, existing skills, integration needs, governance, performance, commercial model, operational ownership and roadmap alignment. A platform assessment can compare suitable options rather than defaulting to one product.
How are security and privacy addressed?
The service can address identity, least-privilege access, network design, encryption, secrets, data classification, masking, retention, residency, logging, monitoring and incident-response requirements. Legal, regulatory and cybersecurity conclusions should be validated by authorised specialists.
What deliverables are typically provided?
Typical deliverables include current-state findings, architecture decisions, target-state diagrams, backlog, landing-zone requirements, data models, pipelines, infrastructure-as-code, test evidence, control mapping, operational runbooks, cost model, training materials and transition documentation.
How long does an Azure data platform engagement take?
There is no reliable fixed duration before discovery. Timing depends on scope, source systems, data quality, integration complexity, security approvals, environments, migration waves, testing, stakeholder availability and whether the work includes implementation or managed support.
What affects the cost of an Azure data platform service?
Cost is influenced by assessment depth, number of sources and use cases, architecture complexity, data volume and velocity, migration scope, environments, security controls, automation, testing, documentation, support model and required specialist roles. Azure consumption charges are separate from consulting fees.
Can the service support real-time data and analytics?
Yes, where justified. Event Hubs, Stream Analytics, Databricks streaming, Fabric real-time capabilities and related services may support event-driven ingestion and low-latency analytics. The design should consider operational complexity, cost, reliability and actual latency requirements.
Does Dataconsultant provide managed Azure data platform support?
Managed support can include monitoring, incident triage, pipeline operations, data-quality checks, cost reporting, release support, access reviews, service improvement and knowledge management. Responsibilities and service levels are agreed during scoping.
What client participation is required?
Clients normally provide executive sponsorship, access to business and technical stakeholders, source-system information, security and policy requirements, environment access, architecture approvals, test participation and timely decisions. Missing inputs and dependencies are recorded as delivery risks.
How are platform outcomes measured?
Measures can include data freshness, pipeline reliability, processing performance, data-quality results, time to onboard sources, cost per workload, incident trends, user adoption, control compliance and delivery of prioritised use cases. Baselines and attribution limits should be documented.