Customer Story

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

Business activity monitoring (BAM) provides end-to-end visibility of business processes, enabling enterprises to make better-informed decisions and quickly address problem areas. A BAM framework is used for continuous event correlation, real-time alerts, and monitoring KPI statistics for business activities in real-time.

Business needs

A US-based Fortune 500 mortgage lender wanted a BAM solution that could apply data reconciliation checks, aggregate data anomalies for reporting, and perform rule-based data quality checks in real-time. They were looking for a solution that would:

  • Power real-time ingestion, event processing, and KPI computing
  • Detect data issues with continuous matching and monitoring
  • Ensure high performance, alerts, and customization


Gathr enabled the client to implement an end-to-end continuous monitoring solution leveraging out-of-the-box drag-and-drop operators and a custom development framework. Using a data-first, code-free approach, Gathr allowed users to effortlessly design and manage complex data flows on a visual canvas.

  • Deployed and managed data pipelines on Apache Spark-based infrastructure
  • Created streaming and batch ETL pipelines for real-time alerting and historical data processing to generate SLA reports
  • Enabled real-time data summary generation at different levels (including historical data) and provided an aggregated view of these
  • Provided 200+ operators for data wrangling and transformation with support for custom extensions and late data arrival
  • Enabled rapid, efficient prototyping, and operationalization of ETL frameworks
  • Delivered an SLA monitoring solution in 3 modules based on different types of pipelines:
    • Data ingestion, quality management, and landing
    • Data correlation and SLA monitoring
    • End of day and month views of ETL processing data


  • Enabled ingestion and processing of 1 TB data on a daily basis
  • 10x faster data processing compared to the existing system
  • Efficient near real-time KPI tracking with code-free business rule updates at runtime
  • Improved visibility through correlation of real-time and historical data
  • Better decision-making with instant SLA status checks

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