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From Legacy Batch Exports to Real-Time Analytics: A Modern Data Foundation in Microsoft Fabric

Industry

Data availability latency
(was 8–24 hrs)

Company Size

Dependency on legacy process

Technologies

Foundation created for analytics, BI, and AI.

Domain Area

Replay capability for failed loads

Business Challenge

A System of Record Trapped Behind a Legacy Pipeline

The client’s warranty management platform served as the system of record but the way data flowed out of it constrained analytics and reporting downstream.

  • Data extractions relied on a legacy batch process, introducing 8–24 hours of latency between when data was generated and when it became available for reporting. 
  • Reporting and downstream analytics placed indirect pressure on live operational systems, creating reliability risk around a mission-critical platform. 
  • There was no governed, scalable ingestion path into the organization’s Azure and Microsoft Fabric analytics environment, limiting trust in downstream data use. 

Failed loads required manual recovery, validation was limited, and advanced use cases such as BI dashboards, fraud detection, and vendor performance analytics remained out of reach. 

Solution

A Governed Lakehouse Ingestion Framework 

Fulcrum Digital designed and implemented a robust data ingestion framework connecting the client’s Kafka-based warranty data exports to Microsoft Fabric, replacing the legacy extraction process with a structured, automated, partition-aware pipeline. 

Governed Bronze Layer 

A single, trusted entry point for warranty data into the analytics environment, structured for Silver/Gold transformation and ready for BI, AI, and data science workloads. 

Partition-Aware Incremental Ingestion 

Historical and daily delta data processed automatically, with only new data ingested each run, eliminating redundant reloads and reducing system pressure. 

Built-In Validation, Monitoring, & Replay 

Schema and row-level validation, error handling, automated alerts, and replay capability built directly into the pipeline to ensure data completeness and reliability, alongside parallel verification against the existing process during transition. 

Operational Readiness & Scalable Design 

Comprehensive data mapping, runbooks, and documentation delivered to support client ownership, while the architecture was designed to onboard additional tables and topics with minimal effort. 
Table comparing data operations before and after modernization: automated partition-based ingestion, data available in under 10 minutes, legacy dependency eliminated, automated replay capability, schema and row-level validation, a Fabric Bronze foundation, and resilient monitored pipelines.
Infographic titled Built for Lakehouse Success: Fabric-Native, Governed, Resilient, Delivered, covering deep expertise in event-export-to-Lakehouse patterns, partition-aware incremental design, resilience built in from day one, and strong delivery discipline.

Benefits

Deep expertise in event-export-to-Lakehouse patterns, with a Fabric-native ingestion approach built around governed data movement rather than generic ETL.
Partition-aware incremental design that reduces reload pressure, supports efficient daily processing, and scales cleanly as data volumes grow.
Resilience built in from day one, with validation, monitoring, alerting, and replay capability treated as operating requirements, not afterthoughts.
Strong delivery discipline across Bronze-layer design, documentation, and transition support, ensuring a clean cutover and long-term client ownership.

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