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Fully Metadata-Driven Architecture
New data sources are onboarded by adding metadata configurations rather than writing bespoke pipelines, reducing development from weeks to days.
A leading UK retail bank consolidated fragmented data sources into a single, scalable Azure lakehouse where new datasets now onboard by configuration rather than by project.
A leading retail bank in the UK was looking to modernize its data warehousing capabilities and consolidate multiple fragmented data sources into a single, scalable lakehouse platform.
Strict data governance requirements that any new platform had to satisfy from day one, not retrofit later.
Transactional, customer and operational data growing exponentially against a warehouse that scaled by procurement.
Legacy systems required high manual intervention, with failed jobs needing someone to notice and rerun them.
Traditional data warehouse enhancements were time-consuming, making every new source a project of its own.
We designed and implemented an Azure-based metadata-driven lakehouse that addressed both the immediate operational pain points and the bank's long-term scalability goals.
New data sources are onboarded by adding metadata configurations rather than writing bespoke pipelines, reducing development from weeks to days.
Intelligent monitoring with automatic detection and reprocessing of failed jobs, without manual intervention or an engineer on call.
Azure's tiered storage and elastic compute used deliberately, holding performance targets while staying inside budget.
Lineage, access control and audit evidence built into the platform itself, so compliance is a property of the system rather than a reporting exercise.
New datasets moved from weeks to days.
Significant infrastructure and operational savings against the legacy estate.
Meeting all financial industry standards.