Data Management

Why it matters

Legacy data warehouses were built for a different era. They were designed for batch reporting, relatively small volumes, and a stable schema. They were expensive to operate and slow to change. They suited their time. But business needs have shifted.

Reports need to be real-time, not daily. Analysts need to explore data freely, not follow rigid data models. AI and machine learning models need high-quality, well-structured training data. Your legacy warehouse wasn’t designed for any of this. Scaling means buying more hardware. Real-time means replacing infrastructure. AI-readiness means redesigning everything.

The real problem isn’t the legacy warehouse. It’s that you’re trying to solve modern problems with infrastructure built for a different purpose. You’re limited by its architecture. You’re constrained by its costs. You’re blocked by its limitations. Every new requirement becomes a project.

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You can migrate your legacy warehouse to the cloud as-is (lift-and-shift), but you’ll replicate all its limitations. Better to redesign around cloud-native architecture—separate storage from compute, use distributed systems, exploit cloud-native features. Azure Synapse and Microsoft Fabric are designed this way. Initial redesign takes longer but long-term costs and capabilities improve substantially. The organisations moving fastest are those that redesign, not those that replicate.

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Retrofitting governance to an existing warehouse is painful. Standards drift. Access control becomes complex. Definitions get duplicated. Better to build governance into the new design from day one. Microsoft Purview integrates with Synapse and Fabric, so metadata, lineage, and access control are built-in, not added later. This foundation saves years of cleanup.

Key Features

Current state assessment and target design

We assess your legacy warehouse—its schema, query patterns, performance characteristics, and technical debt. We understand what’s working (keep it) and what’s limiting you (redesign it). We design the target architecture based on your data volumes, latency requirements, and governance needs. We model costs and performance so you understand the trade-offs.

Cloud platform selection and implementation

We help you decide between Azure Synapse (analytics-focused), Microsoft Fabric (unified analytics), or Azure SQL (relational workloads). Each has different strengths. The right choice depends on your schema, query patterns, team skills, and future roadmap. We implement using cloud-native patterns, not legacy replicas.

Phased migration and parallel running

Most organisations can’t stop using their legacy warehouse on day one. We design phased migrations where new workloads move to the cloud while the legacy system still serves existing reports. We run both in parallel until the cloud platform is proven, then decommission the legacy system. This approach reduces risk and prevents business disruption.

Optimisation, monitoring and cost control

Cloud platforms let you pay for what you use, which is efficient but requires discipline. Poor queries become expensive fast. We implement SCC Vision monitoring to track performance and costs. We tune queries and indexes. We right-size compute resources. We help your teams learn cloud-native optimisation patterns so costs stay predictable.

How it works

Step 1

Assess legacy warehouse and define target architecture

We document your current warehouse—schema, key reports, query patterns, performance bottlenecks, and technical debt. We understand your governance requirements and AI ambitions. We design the target state: which platform (Synapse, Fabric, SQL), what data model, which access controls. We model costs so you understand the investment.

Step 2

Design phased migration and validate parallel operation

Most organisations can’t migrate everything at once. We design phased migrations where highest-value workloads move first. We set up parallel running so the legacy warehouse and new cloud platform coexist. We validate that cloud performance meets your latency requirements before decommissioning legacy systems. This reduces risk substantially.

Step 3

Build cloud data warehouse using Azure/Fabric/Synapse

We build the cloud warehouse using your chosen platform. We use cloud-native patterns—distributed storage, compute scaling, metadata governance. We implement data quality rules, access controls, and SCC Vision monitoring. We load data incrementally, validating quality and performance. Most organisations see significant performance improvements immediately.

Step 4

Migrate reports and analytics to cloud platform

As the cloud warehouse stabilises, we migrate reports and analytics. Power BI connects to the new warehouse. Existing reports continue to work without modification. Analysts get faster query performance and improved self-service capabilities. We provide training so your teams understand the new platform’s strengths and how to optimise their queries.

Step 5

Decommission legacy warehouse and optimise continuously

Once all critical workloads have migrated and proven stable on the cloud platform, we decommission the legacy warehouse. Costs drop significantly. You free up infrastructure budget. We continue monitoring and optimising—tuning queries, right-sizing compute, exploring AI-readiness features. The cloud warehouse becomes the foundation for analytics and AI.

Partners

Specialists

Alexander Viljoen

Digital Data Architect

Alexander helps IT leaders make the right data platform decisions, combining strategy, architecture, analytics and everything in between. He’s guided dozens of organisations through warehouse modernisation—some successful, some painful.

He sees the patterns that work: investing in design upfront, governing data quality from day one, building analytics talent alongside infrastructure. He’s equally comfortable discussing cloud economics, query optimisation, governance frameworks, and AI readiness.

Modernise your analytics foundation

Legacy data warehouses limit what’s possible. Real-time reporting. Self-service analytics. AI-ready foundations. All require moving beyond traditional architecture.

Cloud data warehouses solve this, but the migration matters. The right approach redesigns rather than replicates. Governance from day one. Performance by design. A foundation that scales with your ambitions.

Book a free data assessment to understand where your warehouse is holding you back and what a phased modernisation approach could achieve.

Speak to a specialist

A person standing in a server room holding and working on a laptop, surrounded by racks of illuminated servers.

FAQs

How is a modern data warehouse different from a legacy warehouse?

Legacy warehouses were built for batch reporting with relatively stable schemas and modest scale. Modern cloud-native warehouses scale storage and compute independently, support unlimited data volumes, enable real-time analytics, and integrate governance at the platform level. They’re also significantly cheaper to operate for large scale. The fundamental difference is architecture—legacy is monolithic, modern is distributed.

Can we modernise without disrupting existing reports?

Yes, absolutely. We design phased migrations where new workloads move to the cloud platform while legacy reports continue using the legacy warehouse. We run both in parallel, validating that cloud performance and governance meet your requirements. Once validated, we migrate reports in priority order. Most organisations see zero disruption this way. The legacy warehouse remains available throughout the transition.

Do we have to use Azure, or can we choose Fabric or SQL?

All three are Microsoft platforms that work well together. Azure Synapse is best for large-scale analytics workloads with complex ETL. Microsoft Fabric is best for unified analytics across warehousing, lakehouses, and real-time analytics. Azure SQL is best for traditional relational workloads that don’t need massive scale. The right choice depends on your data model, query patterns, and organisational preferences. We help you evaluate based on your specific requirements.

How do we control costs in a cloud data warehouse?

Cloud platforms charge for what you use, which is efficient but requires discipline. Poor queries become expensive fast. We implement SCC Vision monitoring to track query performance and resource usage. We work with your teams to optimise queries, right-size compute resources, and design efficient patterns. Most organisations see cost reductions of 30-50% once they learn cloud-native optimisation techniques, even after accounting for increased scale.

Is a modern data warehouse required for AI readiness?

Structured, governed data foundations significantly improve AI readiness. Machine learning models need clean data with consistent definitions and clear lineage. If your data management is chaotic, AI projects struggle. A modern warehouse doesn’t guarantee AI success, but it provides the foundations that models need. Most AI-ready organisations started with strong data governance and platform design.