Of data quality issues originate in ETL pipelines
Reduction in analytics errors after ETL test automation
Of companies plan to scale data validation as part of modernization
Of enterprise time is spent debugging bad data
Confirm completeness, mapping, and schema integrity across your entire data pipeline.
Validate complex joins, filters, aggregations, and calculations.
Ensure timestamp-based and CDC logic is accurate.
Catch drift, null violations, and field-level inconsistencies.
Monitor load time, job success, and record latency.
Reconcile dashboard metrics with validated source truth.
ETL testing is not just about verifying record counts. It's about validating business logic, protecting downstream analytics, and enabling confident, compliant decisions.
— Samay Thakkar, Founder and CEO
Source-to-target data validation achieved through comprehensive testing
Faster issue resolution on broken pipelines with pinpoint diagnostic tools
Reduction in business team-reported data errors after implementing ETL testing
Critical business rule coverage tested continuously in production pipelines
