Testing in DE feels decades behind traditional SWE. What does your team actually do?

Summary

Data Engineering teams lag behind on testing methodologies compared to traditional software development, undermining quality standards.

Inconsistent Testing Culture

A Reddit discussion highlights the variety in testing practices within Data Engineering teams. While some teams utilize extensive dbt testing suites and Great Expectations pipelines, a significant number rely on a simpler approach, counting rows and avoiding further checks.

The Market Impact

For BI professionals, this inconsistent testing culture is concerning as it jeopardizes the reliability of data flows and overall data quality. This situation starkly contrasts the rigorous testing methods customary in traditional software development, suggesting a shift towards a more standardized approach in Data Engineering is necessary. Competitors that invest in quality assurance will benefit from the increased reliability of their data.

Emphasizing Quality Assurance

BI professionals need to recognize the urgency of implementing and enhancing testing strategies. This can be achieved by investing in tools and frameworks that enable automated testing, such as dbt and Great Expectations, to ensure a solid foundation for data quality.