Case Study | 14-Aug-2025

Fueling Mortgage Intelligence with a Connected Data Lake 

Faster analytics, seamless integration, and airtight compliance through Databricks-powered architecture.

Elaborate on the Challenge

A large mortgage servicer navigated a fragmented data environment, with critical information dispersed across legacy platforms and third-party applications. This fragmentation led to performance bottlenecks, limited analytical capabilities, and growing compliance risks. The existing systems couldn’t scale to handle increasing data volumes or complex processing needs, making tasks like predictive modeling or integrated reporting cumbersome and slow. Manual processes and inefficient resource utilization drove up costs, while the absence of a unified governance layer made it difficult to track data lineage or ensure regulatory adherence. What the client needed was not just better storage - but a more innovative, scalable, and future-ready data foundation. 

Objective Summary

To centralize all mortgage servicing data into a unified data lake, enabling secure, high-speed analytics, AI/ML capabilities, regulatory compliance, and seamless workflow integration - while reducing operational overhead.   

Business Impact

Built a high-availability, secure data lake enabling sub-minute analytics on large datasets, while reducing processing costs by 20% and accelerating time-to-insight with rapid workflow deployment. 

99.9% system availability with low-latency access  

Sub-minute response times for large datasets  

20% cost savings in processing and storage  

Full regulatory compliance with robust data governance  

Scalable foundation for AI/ML-powered mortgage insights 

Solution - Powering insights through connected, compliant, and intelligent data. 

Moder engineered a comprehensive data transformation program by building a centralized repository using Azure Data Lake Storage Gen2, layered with Delta Lake to support ACID transactions, schema enforcement, and versioning. Apache Spark on Databricks was the backbone for processing large-scale ETL pipelines. At the same time, Unity Catalog was deployed for metadata management, role-based access control, and end-to-end audit trails.  

CI/CD workflows were automated via Azure DevOps to accelerate deployment and ensure sustainability. The Solution was integrated with Azure Synapse to support advanced mortgage analytics use cases - including loan performance forecasting and risk modeling.  

Within the first six months, Moder deployed production-grade ETL and ML pipelines while optimizing compute and storage costs - laying the groundwork for enterprise-wide data intelligence. 

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