The challenge
Manual efforts and evolving schema with outpaced legacy systems
Legacy data systems struggled to handle growing data volumes, evolving schemas, frequent updates, and complex access requirements. Their limited scalability and unreliable refresh capabilities led to stale, inconsistent data. As a result, many businesses resorted to manual processing, causing delays and fragmented insights, ultimately hindering timely decision-making, secure collaboration, and agility across the product lifecycle.
Key challenges
Delayed insights from non-scalable data architecture
High manual overhead in transformation processes
Limited secure access for diverse business units
No clear lineage or governance across data layers
Schema evolution in critical datasets
The solution
Medallion architecture for evolving schema and RLS to govern at scale
Dynamic data modeling
Metadata-driven schema generation
4-hour schema refreshes
Parallel refresh pipelines
Safe transformation
Access and governance
Maintaining mappings for various business and AAD groups for RLS
Audit-ready admin/user group separation
Role-based access and filtering
Implementation approach
1
Robust data architecture for scalable, secure analytic
4-hour refresh
Validated tables
Schema handled
Implementation approach
1
2
3
4
5
The impact
Automating complexity, streamlining data migration, and accelerating outcomes
Looking ahead
Qlik sense integration
Using presentation layer for business reporting with RLS
New changes onboarding
Easy to update any datasets which have schema changes
