01
Current-State Assessment
Review platforms, pipelines, data stores, workloads, controls, costs, bottlenecks, technical debt and delivery practices.
Enterprise Data Architecture
Design a Governed Data Lakehouse for Analytics and AI
Dataconsultant helps data and technology leaders assess, design and plan enterprise lakehouse platforms that unify ingestion, storage, transformation, governance, analytics and AI workloads. The service addresses fragmented data estates, inconsistent controls and scaling constraints through vendor-neutral architecture, practical implementation patterns and a decision-ready roadmap.
- Vendor-neutral architecture decisions
- Governance and security built into design
- Workload-led platform planning
- Implementation-ready documentation
Illustrative Lakehouse BlueprintTarget-state view
Business Apps
Files & Events
External Data
IngestBatch, streaming, CDC and API patterns
BronzeControlled raw data with audit history
SilverValidated, standardised and conformed data
GoldBusiness-ready products and semantic models
BI & Reporting
Data Science
AI Products
Direct answer
What is Data Lakehouse Architecture Service?
A data lakehouse architecture is an enterprise data-platform pattern that brings scalable object storage, open or interoperable data formats, reliable transactions, metadata, governance and high-performance analytics into one coordinated environment. It can reduce unnecessary movement between separate lakes and warehouses while supporting data engineering, business intelligence, machine learning and AI.
It is not a single product or mandatory blueprint. The right design depends on workload characteristics, existing investments, regulatory obligations, operating maturity, performance needs and cost constraints.
Primary purposeUnify diverse data workloads on a governed platform foundation.
Main deliverablesAssessment, target architecture, standards, migration roadmap and implementation controls.
Typical buyersCIOs, CTOs, CDOs, heads of data, analytics leaders and enterprise architects.
Service offering
Architecture Decisions Covered by the Service
The engagement connects business workloads with platform, data-management, governance, security and operating-model decisions.
02
Target Architecture
Define logical and physical architecture, storage layers, compute patterns, domains, data products and integration boundaries.
03
Governance by Design
Embed catalogue, lineage, quality, ownership, access, privacy, retention, observability and audit requirements.
04
Migration Roadmap
Prioritise workloads, dependencies, proofs of concept, transition states, acceptance criteria and operational readiness.
Clarify your lakehouse architecture scope
Discuss workloads, platform constraints, governance needs and migration priorities with a specialist.
Business value
What a Well-Designed Lakehouse Can Support
Consistent data foundation
Reduce disconnected copies, duplicated transformations and conflicting business definitions.
Faster workload delivery
Use reusable ingestion, quality, metadata and deployment patterns across data products.
Governed analytics and AI
Apply traceable controls from source ingestion through analytical and AI consumption.
Transparent platform economics
Connect workload design, storage, compute, service levels and operating practices to cost.
Problems addressed
Common Reasons Organisations Reconsider Their Data Architecture
Separate lakes, warehouses and analytical silos
Repeated data movement, overlapping platforms and inconsistent transformations create avoidable complexity.
Unclear lineage and inconsistent data quality
Teams cannot reliably explain where critical data came from, how it changed or whether controls were applied.
Slow pipelines and unpredictable performance
Architectures struggle with growing volume, concurrency, streaming, semi-structured data and AI workloads.
Security and governance added too late
Access, classification, retention, privacy and audit requirements are handled inconsistently across tools.
High compute, storage and operational spend
Unmanaged workload patterns, duplicated data and limited observability weaken cost accountability.
Architecture that does not reach implementation
High-level diagrams lack standards, decision records, migration steps, ownership and acceptance criteria.
Turn platform issues into an architecture decision plan
We can help separate immediate remediation needs from longer-term lakehouse transformation.
Suitability
Who the Service Is For
Good fit
- Modernising a fragmented analytics and data-engineering estate
- Planning cloud, hybrid or multi-platform data architecture
- Supporting BI, machine learning and AI on shared governed data
- Migrating workloads from legacy warehouses or unmanaged lakes
- Introducing domain-oriented data products or reusable platform services
- Improving lineage, quality, access governance and observability
May not be the right fit
- A narrowly scoped report or dashboard is the only requirement
- A vendor has already fixed the architecture without option assessment
- The immediate need is penetration testing or specialist cyber incident response
- No accountable sponsor or technical stakeholders are available
- The organisation needs only temporary platform administration
- A broader data strategy or operating-model decision must be resolved first
Use cases
Typical Data Lakehouse Architecture Service Scenarios
Cloud data-platform modernisation
Design a target lakehouse while rationalising legacy warehouses, data marts, ETL tooling and storage.
Typical output: option assessment, target blueprint and migration waves.
Enterprise analytics foundation
Create governed data-product and semantic-layer patterns for self-service analytics and executive reporting.
Typical output: domain model, quality controls and consumption standards.
AI and machine-learning enablement
Connect feature preparation, experimentation, training data, model inputs and monitoring to governed source data.
Typical output: AI-ready data architecture and control requirements.
Real-time operational insight
Introduce event, CDC and streaming patterns for lower-latency decisions without bypassing governance.
Typical output: streaming architecture and service-level design.
Data-mesh platform enablement
Provide shared lakehouse capabilities that allow domains to build and operate governed data products.
Typical output: platform services, product standards and federated controls.
Regulated-data consolidation
Improve traceability, classification, access, retention and evidence across sensitive analytical datasets.
Typical output: control architecture and compliance traceability map.
Capabilities
Data Lakehouse Architecture Service Capabilities
A
Workload and non-functional requirement analysis
Assess BI, data engineering, batch, streaming, data science, AI, data-sharing and operational use cases alongside latency, availability, performance, resilience, residency and recovery requirements.
B
Storage, table-format and compute architecture
Evaluate object storage, open table formats, transactional consistency, partitioning, compaction, schema evolution, workload isolation, elastic compute and interoperability trade-offs.
C
Ingestion, transformation and orchestration patterns
Define batch, CDC, API, event-streaming, data-contract, transformation, testing, deployment and recovery patterns suited to source systems and service levels.
D
Metadata, lineage and data-quality controls
Design catalogue integration, technical and business metadata, lineage capture, quality rules, issue workflows, ownership, observability and evidence requirements.
E
Security, privacy and access governance
Specify identity, least privilege, role and attribute-based access, encryption, secrets, masking, row and column controls, audit logging, retention and segregation of duties.
F
Platform operations, FinOps and reliability
Plan monitoring, incident response, capacity, cost allocation, workload policies, backup, disaster recovery, release management, service ownership and continuous improvement.
Deliverables
Typical Architecture Deliverables
Final deliverables are agreed during discovery and tailored to the organisation’s estate, decision stage and implementation responsibilities.
| Deliverable | What it covers | Decision supported | Client input |
|---|---|---|---|
| Current-state assessment | Platforms, workloads, pipelines, controls, skills, costs and technical debt | What to retain, remediate, replace or retire | Inventories, diagrams, issue logs and stakeholder access |
| Requirements and workload catalogue | Functional and non-functional requirements by workload | Architecture priorities and service levels | Business use cases, volumes, latency and criticality |
| Target logical architecture | Data flows, platform services, domains, layers and consumption patterns | Future-state structure and boundaries | Enterprise standards and integration constraints |
| Platform option assessment | Capability, interoperability, risk, cost and operating implications | Shortlist or platform decision | Procurement constraints and existing agreements |
| Governance and control architecture | Ownership, metadata, lineage, quality, access, privacy, retention and audit | Control design and accountability | Policies, obligations and risk appetite |
| Reference patterns and standards | Ingestion, layering, transformations, data products, testing and deployment | Consistent implementation | Engineering practices and toolchain |
| Migration and implementation roadmap | Waves, dependencies, pilots, acceptance criteria, risks and transition states | Mobilisation and investment planning | Priorities, funding, teams and change windows |
| Operating model and RACI | Platform, domain, governance, security and support responsibilities | Ownership and service operation | Organisation structure and sourcing model |
Request a decision-ready lakehouse architecture package
Scope assessment, design and implementation planning around the decisions your teams need to make.
Delivery process
How Dataconsultant Delivers the Service
Discovery and alignment
Confirm business outcomes, stakeholders, scope, constraints and architecture decisions.
Output: agreed brief and evidence request.
Current-state assessment
Review platforms, data flows, workloads, controls, costs, skills and technical debt.
Output: findings, risks and baseline.
Requirements and options
Define workload requirements and assess viable architecture and platform options.
Output: requirements catalogue and option analysis.
Target-state design
Design logical architecture, reference patterns, governance, security and operations.
Output: target blueprint and decision records.
Roadmap and validation
Sequence pilots, migration waves, dependencies, controls and acceptance criteria.
Output: implementation roadmap and risk register.
Mobilisation and assurance
Support implementation, design reviews, knowledge transfer and operational transition.
Output: assurance reports and handover pack.
Technology and standards
Platforms, Technologies and Reference Frameworks
Technology choices are evaluated against workload, interoperability, control, skills, operating and commercial requirements.
Technology ecosystems
Standards and control references
Applicable laws, sector rules and certification requirements must be confirmed by authorised legal, regulatory, security and assurance specialists.
Compare architecture and platform options
Receive a structured assessment of capability, interoperability, risk, cost and operating implications.
Engagement models
Flexible Ways to Engage
| Model | Best suited to | Typical scope | Commercial basis |
|---|---|---|---|
| Architecture assessment | Early-stage decisions or platform concerns | Current state, requirements, findings and options | Defined scope |
| Target architecture project | Approved modernisation initiative | Detailed blueprint, controls, standards and roadmap | Milestone-based project |
| Architecture advisory | Internal teams needing specialist support | Workshops, design reviews, decision support and assurance | Retainer or time-based |
| Implementation support | Teams building or migrating the platform | Patterns, engineering support, QA and operational readiness | Team or workstream basis |
| Managed architecture assurance | Long-running programmes or multi-vendor delivery | Governance, standards, review gates and reporting | Ongoing managed service |
Illustrative example
From Fragmented Data Stores to a Governed Lakehouse
This example is illustrative and does not represent a specific client or guaranteed result.
Starting situation
- Separate operational lake, warehouse and departmental marts
- Repeated batch pipelines and inconsistent transformation logic
- Limited lineage and manual access approvals
- Growing analytics and machine-learning demand
- Unclear workload costs and ownership
1. Assess: Inventory workloads, controls, costs and pain points.
2. Decide: Select target platform roles and interoperability principles.
3. Design: Define ingestion, storage, transformations, products and controls.
4. Transition: Pilot representative workloads and validate patterns.
5. Scale: Migrate in waves with governance and operational measures.
Measurement
Expected Outcomes and Relevant KPIs
Delivery speedTime from source onboarding to trusted consumption.
Data reliabilityQuality-rule pass rates, incident frequency and recovery.
Governance adoptionLineage coverage, ownership, classification and policy adherence.
Platform efficiencyUnit cost, utilisation, duplicated data and idle compute.
PerformancePipeline latency, query response and workload concurrency.
ReuseShared patterns, data products, semantic models and components.
Operational resilienceAvailability, recovery objectives and control closure.
User adoptionActive consumers, workload migration and satisfaction measures.
Pricing
Data Lakehouse Architecture Service Cost Factors
A reliable estimate requires scope and evidence. Fixed public pricing can be misleading because architecture depth and implementation responsibilities vary substantially.
Estate complexity
Number of sources, platforms, domains, pipelines, workloads, regions and existing controls.
Decision depth
Assessment only, option selection, detailed design, proof of concept, migration planning or implementation.
Risk and governance
Security, privacy, residency, resilience, audit, sector regulation and third-party dependencies.
Stakeholder coverage
Business units, engineering teams, governance functions, vendors and review forums.
Documentation needs
Reference patterns, standards, decision records, RACI, roadmap, cost model and control traceability.
Delivery support
Architecture assurance, engineering, testing, migration, training, operational transition and managed support.
Request a scoped estimate
Share your current estate, target decisions and delivery expectations for a written engagement proposal.
Why Dataconsultant
Practical Architecture with Clear Decision Traceability
Business and workload led
Architecture choices are tied to real decisions, service levels and organisational outcomes.
Vendor neutral
Options are assessed against requirements, constraints and operating implications.
Control conscious
Governance, security, privacy, quality and evidence are designed into the platform.
Implementation focused
Blueprints include standards, responsibilities, dependencies, acceptance criteria and transition steps.
Discuss your target lakehouse architecture
Use an initial consultation to identify the most appropriate assessment or design scope.
Assurance
Security, Quality, Privacy and Compliance Considerations
Architecture controls
- Identity, authentication and least-privilege access
- Encryption, keys, secrets and network controls
- Data classification, masking and usage policies
- Catalogue, lineage, quality and observability evidence
- Retention, archival, deletion and legal-hold requirements
- Backup, recovery, resilience and incident response
Important boundaries
Architecture consulting can identify requirements, gaps, decisions and control patterns, but it does not itself provide legal advice, regulatory approval, statutory audit, certification, penetration testing or a guarantee of compliance. These activities require appropriately authorised specialists and client accountability.
Assumptions, evidence gaps, client decisions and third-party dependencies should be documented throughout the engagement.
Testimonials
What Clients Value in Architecture Engagements
Illustrative service-experience statements are presented without claims of independent verification.
★★★★★
“The architecture work connected our analytics, engineering and governance requirements in one practical model. Communication was clear, review feedback was handled carefully, and the final roadmap gave our teams usable decisions rather than only a conceptual diagram.”Enterprise Data Programme Lead
★★★★★
“The team explained platform trade-offs in business language and documented assumptions, risks and dependencies. The quality of the deliverables helped technology, security and finance stakeholders review the same proposal with much less ambiguity.”Technology Transformation Director
★★★★★
“We valued the professional delivery, structured workshops and willingness to revise details after engineering review. The output covered target architecture, controls, ownership and migration sequencing, which made it useful for both procurement and implementation planning.”Head of Data Engineering
FAQs
Frequently Asked Questions
What is a data lakehouse architecture?
A data lakehouse architecture combines scalable data-lake storage with warehouse-style reliability, transactions, metadata, governance and query capabilities. It can support data engineering, analytics, machine learning and AI on a coordinated platform foundation.
What is included in Dataconsultant’s data lakehouse architecture service?
Scope can include discovery, current-state assessment, workload requirements, platform options, target architecture, ingestion and transformation patterns, metadata, governance, security, quality, observability, operating model, cost considerations, migration roadmap and implementation assurance.
How is a lakehouse different from a warehouse or data lake?
A warehouse is typically optimised for structured analytics, while a data lake is designed for flexible storage of diverse data. A lakehouse aims to combine open, scalable storage with transactional reliability, governance and high-performance analytics. It does not automatically replace every warehouse or operational platform.
Does a lakehouse require bronze, silver and gold layers?
No. Medallion layering is a useful pattern for separating raw, validated and consumption-ready data, but architecture should reflect workload, ownership, latency, data-product, quality and regulatory requirements rather than apply a naming convention mechanically.
Which technologies can support a lakehouse?
Possible technologies include Databricks, Microsoft Fabric and Azure services, AWS analytics services, Google Cloud data services, Snowflake capabilities, Apache Spark, open table formats, orchestration tools, catalogues, quality tools and security platforms. Selection should be requirements-led.
Can a lakehouse operate in a hybrid or multi-cloud environment?
Yes, but hybrid and multi-cloud designs introduce additional complexity around connectivity, identity, data movement, latency, governance, observability, portability, residency and cost. The architecture should document which capabilities must be portable and which can remain platform-specific.
How are data governance and lineage incorporated?
The design can define metadata capture, catalogue integration, business and technical lineage, ownership, data contracts, quality rules, issue workflows, access policies, retention, audit evidence and governance responsibilities across ingestion, transformation and consumption.
How are security and privacy handled?
Architecture controls can include identity, least privilege, encryption, network controls, masking, row and column security, classification, audit logging, residency, retention and third-party responsibilities. Legal advice, certification and specialist security testing remain separate activities unless commissioned.
How long does an architecture engagement take?
Timing depends on estate complexity, workload count, data domains, stakeholder access, evidence quality, platform options, regulatory requirements, proof-of-concept needs and implementation planning depth. Stages and dependencies are confirmed after discovery.
How is pricing calculated?
Pricing depends on assessment depth, number of systems and workloads, target-platform choices, governance and security requirements, workshops, documentation, migration scope, implementation support and engagement model. A written estimate follows initial scoping.
Can Dataconsultant help with implementation?
Yes. Support can include platform foundation, ingestion frameworks, transformation pipelines, metadata and lineage, quality controls, security configuration, workload migration, testing, operational readiness, training and architecture assurance.
Can you work with our existing cloud and platform vendors?
Yes. Dataconsultant can work with internal teams, cloud providers, software vendors, systems integrators and managed-service partners. Responsibilities, decision rights, information access, standards and escalation routes should be agreed at mobilisation.
What information should we prepare?
Useful inputs include business priorities, workload inventories, source systems, data volumes, architecture diagrams, platform costs, performance issues, security policies, classifications, regulatory obligations, cloud standards, team skills and access to accountable stakeholders.
How should success be measured?
Relevant measures may include onboarding time, pipeline reliability, query performance, quality pass rates, lineage coverage, policy adherence, platform unit cost, workload migration, data-product reuse, incident recovery and user adoption. Baselines and attribution limits should be documented.
What are the main risks of a lakehouse programme?
Risks include unclear workload priorities, treating a product as an architecture, weak ownership, uncontrolled cost, migration complexity, skill gaps, insufficient metadata, inconsistent controls, vendor lock-in and underestimating operational change. Architecture decisions should make these risks explicit.
Still evaluating your lakehouse options?
Share your current platforms, workload priorities and constraints for a practical recommendation on next steps.