Test Data Transformations with iceDQ: Validate Concatenation Logic Across Tables

In today’s video, we demonstrate how to test data transformations using iceDQ. Specifically, we validate a scenario where data from three separate columns (first name, middle name, and last name) in the source table are concatenated into a single column (name) in the target table. We explore how to set up a custom validation rule to compare the transformed data and ensure the concatenation logic works correctly.

Key Highlights

Testing Data Transformations: Learn how to validate transformations like concatenating multiple columns into one.
Custom Validation Rule: Set up a custom rule to compare source and target data, checking for correct concatenation and handling missing data (e.g., replacing missing middle names with blank characters).
Detailed Error Investigation: Investigate discrepancies between the source and target, identifying cases where the transformation logic didn’t apply as expected (e.g., missing middle names or null values in the target).
Efficient ETL Testing: Automate testing for complex transformations to ensure data integrity across your ETL processes.

With iceDQ, you can automate data validation and testing for transformations, improving the efficiency and accuracy of your data migration and ETL pipelines.

Ready to automate your data transformation testing?
Request a demo today and see how iceDQ can streamline your data testing processes.

Request a Demo: https://icedq.com/request-a-demo
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About iceDQ: Ensuring Reliable Data From Development to Production with iceDQ.

iceDQ is a one-stop platform for data reliability with unified data testing, monitoring, and observability. Large banks, insurance, healthcare, and other enterprises rely on iceDQ in both development and production environments, ensuring data reliability and robust processes.

Streamlined Data Testing in Development: iceDQ is used to automate data migration testing, ETL data pipeline testing, big data lake testing, BI report testing, and more. It helps identify and fix data issues early in the data development lifecycle.

Proactive Monitoring and Observability in Production: iceDQ is used by operations to establish checks and controls for their data pipelines, and the AI-based observability engine ensures anomalies are detected and incidents are reported.
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Request a Demo: https://icedq.com/request-a-demo

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