Approach
G-TECH began with the company’s business outcomes and highest-value analytics, machine learning, and generative AI use cases. The team defined a lakehouse-first roadmap and a governed medallion architecture designed to scale from reliable data engineering to production AI workloads.
Solution
The team implemented a Databricks Lakehouse governed through Unity Catalog, standardized Delta Lake pipelines with incremental processing, and established end-to-end lineage, access controls, and auditability across the data estate.
Results
Processing became three times faster, reducing workloads from hours to minutes. Every dataset is now tracked and governed, and the platform supports advanced analytics and AI at enterprise scale.