Real systems work. Governance, reporting trust, automation, and AI features that can survive production constraints.
INTERACTIVE SANDBOX
Cinematic portfolio environment for governed analytics, data operations, pipeline automation, and operating intelligence.
DATA & ANALYTICS OPERATIONS
Interactive operations dashboard demonstrating how a service owner manages end-to-end D&A operations in a managed services model. Five operational views cover the full scope of data and analytics service delivery.
Built to show fluency in SLA governance, incident management, demand prioritization, cost control, and platform modernization across multi-geo delivery teams.
GOVERNED DATA PRODUCT
Governed, finance-grade revenue dataset on the Databricks Lakehouse with a published data contract and blocking data quality gates.
Productizes the realized-revenue view of sales so Finance and Analytics consume one contracted source instead of reconciling divergent filters across dashboards. Business rules live in the data layer. Data quality signals live on the runs table. Consumers stay thin and governed.
Azure SQL star schema is ingested via JDBC into Databricks. A PySpark transformation applies the Completed-only realized-revenue rule, joins conformed dimensions, and writes a partitioned Delta table at revenue.silver.fact_sales_completed. A published data contract defines ownership, SLA, grain, semantics, and exclusion policy. A blocking data quality task enforces null, grain, status, freshness, and source-to-curated reconciliation checks. Every run logs one row to revenue.ops.fact_sales_completed_runs with SLA status, drift, and failure detail. Power BI consumes the silver table and reads the runs table for a health tile.