Case Study: US-Based Fortune 500 Mortgage Lender achieves 10x faster data processing and real-time KPI monitoring with Gathr

Real-time business activity monitoring for a Fortune 500 mortgage lender

A US-Based Fortune 500 Mortgage Lender needed an end-to-end business activity monitoring (BAM) solution to reconcile data, detect anomalies, and enforce rule-based data quality across a loosely coupled Staged Event-Driven Architecture (SEDA). The lender required high-performance, parallel processing of mixed-format feeds, real-time alerts and gatekeeper rules, error routing for bad data, late-data handling, and the ability to update processing logic and alerts at runtime — requirements that led them to engage Gathr and its no-code/low-code data platform for real-time BAM.

Gathr implemented a continuous monitoring solution using its data-first, code-free visual canvas and drag-and-drop operators, deploying streaming and batch ETL pipelines on Apache Spark infrastructure (ingesting from ESB into HDFS and Elasticsearch) and providing event correlation, rule-based alerts, schema detection, and pipeline orchestration. The Gathr solution processed 1 TB/day, delivered 10x faster data processing than the prior system, enabled near real-time KPI tracking with runtime rule updates, and improved visibility and SLA monitoring through correlated real-time and historical views.