Modern Data Architecture Services

Not if it’s done correctly. We use a parallel migration approach – building the new platform alongside your existing systems and migrating specific data domains one at a time. Your business continues running on current systems until the new architecture is fully tested, validated, and ready to take over. Cutover happens when the data confirms it’s ready, not when the project schedule says so.

Yes – and in most cases it’s more secure than the on-premise environment it replaces. Modern cloud providers deliver physical security at a level most enterprises can’t match internally. We add the logical security layer: encryption at rest and in transit, role-based access controls, automated governance policies via Microsoft Purview, and compliance alignment for GDPR, HIPAA, and SOC 2. Security is designed into the architecture from the start – not retrofitted after the fact.

AI models require clean, consistently structured, historically complete data with traceable lineage. Legacy systems typically hold fragmented, duplicated, and undocumented data that produces unreliable model outputs – regardless of how sophisticated the AI layer on top is. A modern Lakehouse or Data Fabric architecture creates the governed, high-quality data foundation that gives your AI initiatives reliable inputs and your engineers a platform they can actually build on.

We design modular architectures that separate compute from storage and use open standards – Delta Lake, Apache Parquet, and containerized workloads – that are portable across cloud environments. While we often recommend Azure for enterprises already in the Microsoft ecosystem, we structure the architecture so migrating to a hybrid or multi-cloud setup remains a viable option if your strategy changes.

They solve different problems and are often used together. A Data Warehouse is optimized for structured, finance-grade reporting – fast queries, governed metrics, board-ready numbers. A Lakehouse combines the flexibility of a data lake with the governance of a warehouse, making it the right foundation for AI, ML, and mixed structured/unstructured workloads. A Data Fabric is an integration and governance layer that connects multiple platforms and domains into a unified ecosystem – it doesn’t replace your warehouse or lakehouse, it orchestrates them. For most of our clients – multi-ERP manufacturers and global enterprises with complex integration landscapes – the answer is a hybrid: Lakehouse as the analytical core, warehouse layer for finance-grade reporting, and Data Fabric connecting the rest.

It depends on scope and starting point. A focused modernization of a single data domain – migrating one legacy system to a cloud platform with governance in place – can be delivered in 8 to 12 weeks. A full enterprise architecture transformation covering legacy migration, platform build, Data Fabric integration, governance implementation, and FinOps optimization typically runs 6 to 18 months as a phased program. The assessment phase produces a realistic timeline based on your actual environment – we don’t give generic estimates before we understand what we’re working with.

Directly and specifically. A Lakehouse architecture provides the governed, lineage-tracked data layer that AI models need for reliable training and inference. Data Fabric integration ensures your ERP, CRM, and operational data reaches AI pipelines in a consistent, clean format. For GenAI use cases – RAG applications grounded in your operational documents, predictive maintenance pipelines, or multi-ERP revenue forecasting – the architecture determines whether the initiative produces reliable outputs or expensive hallucinations. We design the data foundation with your specific AI use cases in mind from the start, not as a retrofit after the models are already built.