Cloud & Data Architecture
We design data architectures connecting operational systems, storage, transformation, governance and analytics consumption. Our approach balances flexibility, reliability, security and ongoing cost.
The final solution depends on the client’s environment and assessment.
Common enterprise challenges
We design data architectures connecting operational systems, storage, transformation, governance and analytics consumption. Our approach balances flexibility, reliability, security and ongoing cost.
Common enterprise challenges
- Integrations become fragile and difficult to maintain.
- Full datasets are reprocessed unnecessarily, raising costs.
- Lineage from raw information to analytical datasets is missing.
- Access control, monitoring and recovery processes are insufficient.
From technical complexity to business value
We assess sources, volumes and business requirements before defining data layers, interfaces and orchestration. We design incremental processing, security and observability models, along with quality controls and cost-management practices.
Capabilities we can develop
- Reference architecture and cloud option assessment.
- RAW, CURATED and consumption layer design.
- Incremental ingestion, delta patterns and controlled loads.
- ETL/ELT pipelines, orchestration and automated validation.
- Cataloging, permissions, encryption, quality and lineage.
- Observability, performance, resilience and cost controls.
Consulting outputs you can expect
Illustrative scope; exact deliverables are agreed for each engagement.
Target architecture
Layers, components, flows and design decisions.
Integration blueprint
Delta patterns, dependencies and orchestration.
Security & operations
Roles, monitoring, audit and recovery.
Cloud roadmap
Priorities, costs and evolution phases.
How we structure an engagement
Discover
Understand business processes, systems, data and actual needs.
Design
Agree architecture, accountability, priorities and acceptance criteria.
Deliver
Build or improve processes and solutions through measurable iterations.
Enable
Transfer knowledge, measure outcomes and plan ongoing improvements.
Technology guided by the business objective
AWS S3 and Glue, cloud analytics and storage services, SQL, Parquet and orchestration tooling, depending on client requirements.
Important questions before getting started
Should every dataset move to the cloud?
Not necessarily. Hybrid architectures may suit security, cost or legacy system constraints.
How can processing costs be controlled?
Volume, refresh frequency, incremental loads, partitioning and consumption monitoring are evaluated before operating the solution.
Services that complement this solution
Is your organization facing this challenge?
Tell us about your context and priorities. An initial assessment can help establish scope and define next steps.