Hybrid data architecture | Connect SAS, Microsoft and Databricks with LACO
Large organisations rarely rely on a single data platform. SAS, Microsoft and Databricks each bring unique strengths, but when they operate separately they create silos, duplicated work and inconsistent reporting. A hybrid data architecture brings these platforms together in one governed and connected ecosystem.
LACO helps organisations design such architectures with a pragmatic and structured approach that aligns people, processes and platforms around one shared data strategy but without paying the integration tax of getting it all together.
The challenge
SAS, Microsoft and Databricks each offer value, but without a clear architecture they operate in isolation. Teams move data manually, ETL processes are repeated on different platforms and reports no longer match. Authentication rules differ, lineage is inconsistent and on premise tools are difficult to integrate with cloud environments. This creates delays, frustration and rising cost without delivering real progress. Organisations do not want to replace tools that work. They want an architecture that connects them.
The solution
The first step is understanding the full landscape. LACO performs a complete assessment of tools, processes and dependencies to reveal how data moves today. We then design a modular landscape and challanges that assigns clear roles to each platform. Integration is achieved through secure APIs, automated pipelines and consistent naming and governance standards.
We establish a shared governance layer that covers access rights, metadata, lineage and documentation so all platforms behave as one ecosystem. Change management ensures that IT, business users and data owners understand and trust the new setup.
Results
The organisation gains a connected, governed and scalable platform where everything works together rather than in parallel. The architecture eliminates duplicated work and ensures that data is traceable and compliant across all environments. Teams collaborate more effectively and speak the same data language.
Performance improves through automation, cost overlap decreases and the ecosystem becomes ready for AI and modern analytics. Instead of replacing existing investments, the organisation extends their value with clarity and control.
