Centralizing segmented operational ledgers, multi-marketplace nodes, and WMS assets into an unified high-performance business intelligence lakehouse platform.
Native ERP environment analytical interfaces frequently freeze or deliver heavily bottlenecked data outputs when trying to parse extensive transactional history across cross-system channels. Pulling separate performance metrics from isolated WMS paths, disjointed third-party marketplaces, and ledger accounts forces administrative teams into hours of manual data wrangling.
This structural dependency on fragmented Excel spreadsheets opens up massive formatting entry error loops, creating persistent blind-spots during trend evaluation and delaying leadership's access to vital company margin insight bars.
We engineered an optimized data platform pipeline that extracts NetSuite transaction subsets smoothly via high-volume JDBC/API endpoints into a central Databricks lakehouse. Our script layer handles continuous, automated ingestion—scrubbing, normalising, and joining disjointed warehouse logs and e-commerce records securely.
By compiling aggregated, highly performant semantic databases, we deployed advanced Power BI analytical models that trigger near real-time interactive report cards directly to leadership dashboards.
We designed a highly efficient star-schema data modeling structure that optimizes high-velocity historical analytical reports without driving up database cloud resource costs. Request the internal map.
Drastically increased daily leadership data engagements by providing fluid, instant interaction loops.
Empowered multi-departmental operations stakeholders to self-serve strategic cross-system data rows safely.
Enabled precise, visual predictive tracking to help leadership secure continuous supply chain advantages.
