Accelerating ETL Testing for a Leading Regional Bank

Tech Stack

Database Ecosystem

Multiple relational databases (Source & Target)

‍

Testing & Issue Tracking

GitHub Actions

‍

Data Volume

1140+ ETL jobs, processing thousands of records per job

A leading regional financial institution with billions in assets was facing significant delays in ETL testing cycles due to time-consuming manual validation processes. The inefficiencies in data movement testing were impacting operational efficiency and delaying critical business reports.

The Challenge

High Testing Overhead: Each ETL job execution required extensive manual effort for schema checks, data value validations, and bug tracking.
Prolonged Execution Time: A single test cycle took over an hour per ETL job, making end-to-end validation time-consuming and resource-intensive.
Lack of Automation & Standardization: Bug detection, issue tracking, and test case documentation were repetitive and error-prone.
‍
‍

The Solution

Automated ETL Testing with Datachecks to validate schema, data integrity, and anomalies in under 5 minutes per ETL job.
AI-Powered Anomaly Detection to proactively flag inconsistencies and eliminate manual data comparisons.
Seamless Integration with ADO, automating bug reporting and test case execution.
Dynamic Data Sampling to reduce manual data value checks while ensuring 100% accuracy.
‍

‍

The Impact

98% Reduction in Testing Time—from over an hour to less than 5 minutes per ETL job.
Zero Manual Data Validation—automated schema & data integrity checks.
75% Drop in Repetitive Effort, allowing QA teams to focus on critical tasks instead of repetitive validations.
Faster Go-Live for data-driven banking applications, enhancing business agility.

‍

75% Drop in Repetitive Effort

Zero Manual Data Validation

98% Reduction in Data Downtime

‍