Dataconsultant helps organisations assess, design, build, migrate, govern and operate data platforms on Amazon Web Services. The service connects business use cases with secure cloud architecture, dependable data pipelines, controlled access, analytics readiness and measurable operations, giving technology and data leaders a practical route from fragmented estates to a scalable platform.
Example only. Final service selection depends on workload, risk, performance and cost requirements.
An Amazon Web Services data platform service is a structured advisory and delivery engagement for creating or improving the cloud capabilities used to ingest, store, process, govern and serve organisational data. It can cover architecture, engineering, migration, security, data quality, metadata, analytics, cost management and operating processes. The aim is not simply to deploy AWS services, but to establish a dependable platform that supports agreed business use cases, controls and service levels.
The engagement focuses on practical platform constraints that prevent teams from delivering trusted data efficiently.
Consolidate uncontrolled point solutions into reusable ingestion, storage and transformation patterns with documented ownership and lifecycle controls.
Improve data onboarding, processing, quality validation and governed consumption so analysts and applications receive appropriate data with known freshness and lineage.
Design identity, encryption, logging, network, retention and access-control measures that can be evidenced and operated consistently.
Introduce workload sizing, lifecycle policies, observability, query optimisation, tagging and unit-cost reporting to support better operational decisions.
Current-state review, workload analysis, non-functional requirements, target architecture, platform standards, decision records and implementation sequencing.
Batch, API, event, streaming and change-data-capture patterns with orchestration, schema handling, replay, dependency management and monitoring.
Data-zone structure, open formats, lifecycle policies, partitioning, catalogue integration, retention, archival and controlled sharing.
Reusable transformation frameworks, semantic structures, data contracts, testing, quality rules and product ownership aligned to consumer needs.
Warehouse, query, BI, API and machine-learning consumption patterns with performance, concurrency and service-level considerations.
Identity, access, encryption, metadata, lineage, observability, incident handling, release controls, cost management and operational reporting.
| Deliverable | What it includes | Client input required |
|---|---|---|
| Current-state assessment | Estate inventory, pain points, risks, technical debt, cost and readiness findings. | Architecture, workloads, cost data, incident history and stakeholder access. |
| Target architecture | Logical and deployment views, service choices, integration, security and environment model. | Business use cases, non-functional requirements and enterprise standards. |
| Governance and control design | Ownership, access model, metadata, quality, lineage, retention and assurance controls. | Policies, regulatory obligations, role model and risk appetite. |
| Engineering foundation | Reusable infrastructure, pipeline patterns, CI/CD, testing, observability and documentation. | AWS access, network readiness, source connectivity and delivery standards. |
| Migration and implementation plan | Prioritised waves, dependencies, cutover approach, validation and rollback considerations. | Source priorities, outage constraints, owners and acceptance criteria. |
| Operating model and handover | Runbooks, service measures, support boundaries, training and improvement backlog. | Support model, team capacity, escalation routes and service expectations. |
Objective: connect business outcomes, workloads, stakeholders and constraints.
Output: agreed scope, decision criteria and evidence plan.
Objective: examine architecture, data flows, controls, costs and operating issues.
Output: findings, risks, dependencies and readiness baseline.
Objective: define architecture, service patterns, governance and non-functional requirements.
Output: target design and decision records.
Objective: implement foundations, pipelines, data products and migration waves.
Output: tested platform capabilities and controlled releases.
Objective: verify quality, security, performance, recoverability and acceptance criteria.
Output: test evidence, control findings and remediation actions.
Objective: establish operations, ownership, reporting and continuous improvement.
Output: runbooks, training, service measures and backlog.
The final technology combination is selected from workload evidence, enterprise standards and operating constraints.
Least privilege, role design, federation, service identities, segregation of duties and controlled approvals.
Encryption, key management, classification, masking, retention, deletion and data-residency decisions.
Metadata, lineage, data contracts, validation rules, issue ownership and evidence for material data flows.
Logging, monitoring, incident response, change controls, backup, recovery, vulnerability and configuration reviews.
Applicable legal, regulatory, privacy and sector requirements must be confirmed by authorised client specialists for the relevant jurisdictions.
| Model | Best suited to | Typical scope |
|---|---|---|
| Focused assessment | Leaders needing evidence before committing to a programme. | Current state, risks, options, priorities and next-step recommendations. |
| Architecture and roadmap | Teams preparing a greenfield build or modernisation. | Target design, standards, controls, migration approach and implementation backlog. |
| Implementation workstream | Organisations requiring accountable delivery of defined capabilities. | Platform foundation, pipelines, data products, testing and handover. |
| Embedded specialists | Internal teams needing additional architecture, engineering or governance capacity. | Role-based support integrated with client delivery governance. |
| Managed platform operations | Teams seeking ongoing monitoring, support and improvement. | Operations, quality monitoring, incidents, releases, cost reviews and reporting. |
Source count, data volume, formats, dependencies, integration patterns and technical debt.
Security classification, jurisdictions, audit evidence, resilience, privacy and regulatory obligations.
Assessment, detailed design, build, migration, testing, training, operational support and service levels.
| Measure | Purpose |
|---|---|
| Pipeline success rate | Tracks dependable scheduled and event-driven processing. |
| Data freshness | Measures availability against agreed consumer needs. |
| Time to onboard a source | Shows delivery repeatability and platform usability. |
| Recovery performance | Tests resilience and operational readiness. |
| Measure | Purpose |
|---|---|
| Data-quality pass rate | Monitors conformance of critical data to defined rules. |
| Policy-compliant access | Assesses controlled and timely data consumption. |
| Active consumers | Indicates adoption by analytics and operational users. |
| Unit cost by workload | Supports transparent FinOps and optimisation decisions. |
Discuss your business use cases, current estate, control requirements and delivery constraints with Dataconsultant.
Dataconsultant combines data architecture, engineering, governance, assurance and operating-model expertise so platform choices remain connected to business value, control requirements and sustainable delivery.
Recommendations are based on workloads, risks, dependencies, costs and measurable service requirements.
Architecture and engineering work is connected to priority decisions, users and operating outcomes.
Design rationale, runbooks, controls, ownership and improvement actions are prepared for continued operation.
The service can include discovery, current-state assessment, target architecture, AWS account and network alignment, data ingestion and transformation design, storage and lakehouse patterns, analytics enablement, security controls, governance, observability, migration planning, implementation support, testing, documentation, and knowledge transfer. Final scope depends on business priorities and the existing estate.
Relevant services may include Amazon S3, AWS Glue, AWS Lake Formation, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, AWS Database Migration Service, AWS Step Functions, Amazon MWAA, AWS Lambda, Amazon CloudWatch, AWS CloudTrail, AWS IAM, AWS KMS, Amazon QuickSight, and supporting networking services. Selection is based on requirements rather than a fixed stack.
Yes. It can support greenfield platforms where an organisation needs architecture, landing-zone alignment, ingestion patterns, governance, security, cost controls, environments, delivery standards, and an implementation roadmap before scaling data workloads.
Yes. The engagement can assess fragmented pipelines, duplicated storage, ageing ETL, slow analytics, weak controls, performance constraints, and rising cloud costs, then define a prioritised modernisation plan and support implementation in manageable waves.
Security and privacy are considered through identity and access design, encryption, key management, network boundaries, logging, data classification, least privilege, segregation of duties, retention, masking or tokenisation, incident evidence, and applicable data-residency requirements. Legal and regulatory interpretations remain subject to authorised client review.
Governance can include domain ownership, data-product accountability, catalogue and glossary design, metadata standards, lineage, data-quality rules, access workflows, policy enforcement, exception handling, retention controls, and operating procedures. AWS-native capabilities may be combined with existing enterprise governance tools.
Yes, where justified by the use case. The architecture can combine scheduled batch processing, event-driven ingestion, streaming pipelines, change data capture, near-real-time analytics, and operational data sharing. Service levels, latency, recoverability, and cost trade-offs are agreed explicitly.
There is no reliable fixed duration before discovery. Timing depends on data-source count, data volumes, security reviews, account and network readiness, migration complexity, regulatory requirements, stakeholder availability, testing needs, delivery capacity, and whether the work covers advisory only or full implementation.
Cost depends on scope, architecture complexity, migration volume, number of environments, integration needs, governance depth, security requirements, availability targets, delivery model, and client readiness. Dataconsultant can structure work as a focused assessment, fixed-scope design, implementation workstream, embedded team, or managed service.
Cost controls can include workload sizing, storage lifecycle policies, serverless and reserved-capacity choices, environment scheduling, tagging, budgets, anomaly detection, query optimisation, data-retention rules, unit-cost measures, and FinOps reporting. Illustrative estimates must be validated against actual usage patterns.
Yes. Delivery can be coordinated with internal architecture, cloud, data engineering, security, risk, procurement, application, and analytics teams, as well as AWS and third-party implementation partners. Responsibilities, dependencies, acceptance criteria, and escalation routes are documented.
Useful inputs include business priorities, source-system inventories, data volumes, latency needs, current architecture, AWS account structure, security policies, network constraints, regulatory obligations, service-level expectations, existing contracts, cloud-cost information, delivery plans, and access to accountable stakeholders.
Measures can include pipeline reliability, data freshness, processing time, query performance, data-quality pass rates, incident volume, recovery performance, user adoption, time to onboard a source, policy compliance, access turnaround, platform unit cost, migration progress, and delivery of agreed business outcomes.
Managed support can be scoped for platform monitoring, pipeline operations, incident triage, data-quality monitoring, release support, cost reviews, access administration, service reporting, backlog management, documentation, and continuous improvement. The operating boundary and service levels are agreed in advance.
Common risks include unclear business priorities, weak ownership, over-engineering, uncontrolled cloud spend, insufficient source-data understanding, inadequate security review, poor metadata, migration cutover issues, skills gaps, vendor lock-in, and unsupported performance assumptions. These risks are addressed through evidence, decision gates, testing, and accountable governance.
