CASE STUDY
Fragmented Data Silos
The data about assets was distributed in different platforms and there was no single view hence it was hard to monitor inventory, purchases and transfers on a holistic basis.
Limited Real-Time Insights
Updates could not be received within 24 hours to 48 hours, and this is too long a time to allow a company to make timely business decisions.
Error-Prone, Manual Reporting.
Legacy systems required a lot of manual work to create reports, leading to inconsistencies and delays in critical business decisions.
No Extensibility for AI
The current architecture was unable to support AI-based forecasting or predictive capabilities to restrict future innovation.
Inefficient ETL Processing
The transformations of data were characterized by the high latency and poor workflows that held back the insight and strained resources.
Nonexistent Data Governance or Lineage.
The environment lacked data quality checks, lineage tracking, or alerting mechanisms, making it difficult to trust or audit reports.
100%
75%
100%
Future-ready
Unified Microsoft Fabric Lakehouse
Deployed a Medallion architecture (Bronze, Silver, Gold layers) to automate data ingestion, transformation, and reporting on a single unified platform.
Automated 3-Hour ETL Pipelines.
Introduced Fabric Pipelines that run to extract the data of ServiceNow assets after every 3 hours, initiating data latency within a range of 48 hours to close to real-time.
PySpark-based Data Processing.
PYSpark notebooks used to orchestrate batch processes on a larger scale, and these accord the reusability of data engineering processes flexibly with schema enforcement and Delta Lake storage.
End-to-End Power BI Reporting
Provided automated dashboards such as Inventory Snapshot, Financial Overview, Open Purchase Orders and Open Transfer Orders, refreshed after every three hours, and not manually updated.
Schema Enforced Data validation.
Applied schema validation, type casting, and deduplication logic across all data layers, ensuring consistent, accurate, and trusted information.
Automated Governance & Monitoring.
Predefined metadata validation, failure notification, and lineage of all data, allowing to proactively detect and fully audit.
Processing Engine | PySpark Notebooks,Delta Lake, Microsoft OneLake
Ingestion Engine| Microsoft Fabric Pipelines (3-hour intervals)
Lakehouse Layers | Bronze, Silver, Gold
Data Source | ServiceNow via Mid-Server (CSV Extraction)
Monitoring | Email alerts, metadata validation, failure detection
Visualization | Power BI (SQL Analytics Endpoint)
Executive Insights
- Texas Roadhouse required an efficient channel to track assets across hundreds of restaurant locations. Legacy reporting took 1–2 days, relied on manual effort, and produced inconsistent data.
- Royal Cyber implemented Microsoft Fabric with automated pipelines that refresh every three hours. ServiceNow data now flows through a Medallion lakehouse directly into Power BI dashboards.
- Data latency dropped from 48 hours to under 3 hours. Manual reporting was eliminated. Data accuracy improved with schema-enforced validation. Full governance with lineage and automated alerts now in place.
75%
Faster data delivery with latency reduced from 24–48 hours to under 3 hours
Audience
- Executives, CTOs, Director
- IT Consultants
- Business Analysts
- Project Managers
- IT Leaders managing multi-location operations
- ETL & Integration Architects
