Data Integration Testing

Data integration testing ensures the accuracy and reliability of combined data from diverse sources and technologies. BiG EVAL streamlines this process, delivering enhanced data quality and empowering organizations to make confident, data-driven decisions.

End-to-end data validation with BiG EVAL

End to End Data Validation

Effortlessly monitor data accuracy from multiple sources and technologies all the way to your target database using BiG EVAL's end-to-end data validation capabilities. 

Compare and validate information in all layers of your integration architecture with one simple test case and apply different validation algorithms and best practices testing.

All of that during development but also in runtime environments for ongoing quality monitoring.

Supported Test Concepts

Unit Testing

BiG EVAL validates single components or functionality within the data integration process. It ensures that each individual step, such as data extraction, transformation, or loading, works as intended and meets the defined requirements, enabling the detection of issues at an early stage and facilitating efficient troubleshooting.

Integration Testing

BiG EVAL validates the seamless interaction between different data sources and components. It ensures that data is accurately combined, transformed, and transferred from source systems to the target database, while maintaining its quality, consistency, and correctness.

Data Plausibility Checks

BiG EVAL helps verifying that the combined data from various sources is reasonable, consistent, and makes sense within the context of the target system. This step helps identify and correct any inaccuracies, inconsistencies, or outliers in the integrated data, ensuring the quality and reliability of the final output.

Acceptance Testing

BiG EVAL helps verifying that the integrated data meets predefined criteria and business requirements. This testing stage ensures that the data integration solution is functioning as expected and is ready for deployment in a production environment.

Regression Testing

BiG EVAL makes you capapable of automatically retesting previously tested data integration components after changes, updates, or bug fixes have been made. This ensures that existing functionality remains intact and no new issues have been introduced, maintaining data quality and consistency throughout the integration process.

Performance Testing

BiG EVAL assesses and measures the efficiency, speed, and scalability of data integration processes. It involves evaluating factors such as data load times, transformation speeds, and system response times under various conditions to ensure optimal performance and resource utilization, leading to a smooth and reliable data integration experience.

Testing in a data integration development project

Project Quality Assurance on Autopilot

Experience seamless, automated testing during data integration development, ensuring every aspect of your process performs flawlessly and instills confidence in your data-driven outcomes.

  • Regression Testing in Development Environments
  • Testing in Continuous Integration / Deployment Processes
  • Quality Assurance in QA Environments

Agile Data Warehouse Development
with automated test cases

Uncover the success story of Ifolor AG, a company that used BiG EVAL to validate data and gain trust in its decision-making process. See how data quality assurance can lead to a good gut feeling and get inspired by this compelling reference story today!

Monitoring Runtime Environments

Proactively identify and resolve data quality issues in a productive runtime environment boosting confidence in the reliability and accuracy of your data and ensuring a higher level of trust among stakeholders.

  • Continuously validate data before errors arise.
  • See exactly where errors come from.
  • Automatically coordinate tasks to fix issues.
  • Raise Service Desk Tickets automatically.

What our Customers say

Testing and quality assurance in warehouse development is not as advanced as in other software development areas. BiG Eval is a big step in the right direction. Easy-to-use yet powerful scripting. Nice web frontend. Quick and helpful support.

Nicolas F.

Project Manager @ Dillinger

BiG EVAL helps to discover possible data quality issues of our data feeds earlier that before. This enhances the overall data quality of our applications and reduces time and effort for the fixing of issues. BiG EVAL helped us to check the quality of migrated code and therefore significantly reduce the testing efforts. Nowadays, we primarily monitor our daily data processing jobs, what helps us to discover possible data quality issues much earlier. We are very happy with BiG EVAL.

Holger H.

We compared various other test tools and except for BiG EVAL, none of them was capable of fetching meta data and using it in the test case definitions what makes it so powerful.

Raphael B.

Principal Consultant Data & Analytics, Member of the board, Partner @ IT-LOGIX

Thanks to data warehouse automation and test automation, our small team successfully developed an agile, future-oriented BI ecosystem. Without these automations, this would have been unimaginable.

Video: Success Story Presentation (German)

Sören S.

Project Manager BI @ ifolor AG

BiG EVAL helped us stabilize the quality of our data. They met all of our requirements in full: an uncomplicated and reasonably-priced introduction and a high level of integration in our DQM processes and infrastructure.

Günther E.

QA-Manager @ Helsana

Same-day productivity

Achieve instant success with our ready-to-use, expertly designed best practices templates, propelling you toward excellence from day one.

Hundreds of ready-to-use test cases and templates kick-start your testing and quality assurance. Download, configure and use them immediately.

FREE eBook

The Ultimate Guide - Creating a Winning Data Warehouse Test Concept

You want a Simple and Reliable Data Test Concept?

Download our latest ebook, "Creating a Winning Data Test Concept" and learn how to create a simple yet effective testing strategy.