This post describes a survey-product customer whose database-driven configurations often failed to copy correctly when moving data from Development to Stage to Production environments, causing surveys to not push properly to mobile devices and creating customer dissatisfaction. Using ETL Validator, the customer was able to easily compare data across all three environments, catching failed record copies early. This reduced support time spent tracking down missing parameters by more than 60% and improved customer satisfaction.
Key Takeaways
- Transactional systems need cross-environment validation too — while most ETL testing use cases involve data warehouses, this example shows the same comparison approach applies to transactional, configuration-driven systems.
- Silent copy failures caused real customer impact — when records failed to transfer properly from Dev to Stage to Production, surveys didn’t push correctly to mobile devices, directly affecting the end-user experience.
- Manual troubleshooting was time-consuming — before automation, support teams had to manually uncover which specific parameters failed to copy over to production, a slow and reactive process.
- ETL Validator cut support time by over 60% — automating cross-environment data comparison let the customer catch discrepancies early, reducing both support burden and customer complaints.
Most of the use cases that we encounter are for DW scenarios. However, recently, we are also seeing many scenarios for testing transactional systems. This blog is to highlight on one such example.
The customer offers a survey related product and the configuration/setup is driven primarily by database level configurations across many tables. They provide a means for customers to develop using a “Development” database, once everything looks good, move the data to “Stage” and then to “Production” once UAT is complete. While this process may seem pretty straight forward, one of the key challenges for them is that when they push the data from lower environments to upper environments, few records may fail to get copied over properly and the surveys were not getting pushed as expected to mobile devices. This was resulting in customer unhappiness and more time for support to uncover the parameters that failed to get copied to production.
Now, with ETL Validator, they are able to easily compare the data across Dev, Stage and Production, increase customer satisfaction levels and minimize support time by more than 60%.
ABOUT DATAGAPS:
Our vision is empowering our customers with trustworthy Business Intelligence and Data.
Who We Are:
Trusted Globally by more than 70 Companies for Data Test Automation Solutions
Datagaps is passionate about data-driven testing automation. Our flagship solutions, ETL Validator, Data Flow & BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos etc. data warehousing projects and BI platforms.
Mission Statement:
Datagaps was started in the year 2010 with the mission of building trust in enterprise data and reports. We provide software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, and BI Test Automation. Datagaps is an innovative company focused on providing the highest customer satisfaction.
FAQs: Transactional Data Validation Across Environments
1) Why do transactional systems need data validation between environments?
Transactional systems often promote configuration and application data from Development to Stage and Production environments. Data validation ensures records are transferred accurately and completely, preventing missing or inconsistent configurations that could disrupt business processes or impact end users.
2) What problem was the customer experiencing before using ETL Validator?
The customer relied on manual investigation to identify configuration parameters that failed to move between environments. This reactive process increased support effort, delayed issue resolution, and negatively affected customer satisfaction when surveys failed to reach mobile devices correctly.
3) How did ETL Validator help resolve this issue?
ETL Validator automated comparisons between Development, Stage, and Production environments, quickly identifying missing or mismatched records before deployment. This proactive validation reduced support effort by more than 60% while improving deployment reliability and overall customer satisfaction.
Rajesh Kumar A
Digital Marketing Manager, Datagaps
Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.
S P S Murthy Akella
Director, Technology Strategy, Datagaps
Director of Technology Strategy at Datagaps. Business solutions architect and Certified Scrum Master in data engineering, responsible AI, and ML across BFSI, telecom, aviation, and energy.
