Unified Streaming and Batch Data Architectures for Mortgage Technology: A Scalable Reference Mode

Abstract

The mortgage industry requires data-driven decision-making to enhance the effectiveness, efficiency, and customer experience. However, the traditional batch-based architecture cannot scale to meet the demands of real-time analytics and event-driven processing in modern mortgage platforms. This review analyzes the evolution and integration of streaming and batch data-processing models and provides a reference architecture for mortgage technology systems. The current research is anchored on a systematic analysis of the existing models such as Lambda, Kappa and Unified Architectures, which accentuate the key design principles such as scalability, low latency, consistency and compliance, which are the drivers of the next generation mortgage data ecosystems. The proposed reference model will integrate real-time ingestion (Kafka) and processing (Spark/Flink) with storage (Delta Lake/Iceberg) to support both historical and real-time analytics. Performance and scalability analysis is taken care of in the underwriting as well as fraud detection and regulatory reporting. The review will serve as an initial guide for financial institutions transitioning to hybrid, cloud-native, and intelligent mortgage infrastructures.