DATA INTEGRATED. DECISIONS ELEVATED.
Multi Layer Data Pipeline QA Automation
Overview
D&I Integrators designed and implemented an automated QA framework for a life insurance provider to validate data integrity across three stages of data movement and transformation. The framework validates requirements across all three stages, performing row count, schema, field to field, and distinctness validation checks to ensure reliable data quality throughout the Synapse pipeline. This engagement led to D&I securing staff augmentation of data engineering services.
The Challenge
Data moved through three separate source systems and multiple pipeline stages on its way into the Synapse environment, with no consolidated, automated way to confirm it stayed accurate and consistent at each step. Without systematic validation, quality issues could reach downstream reporting before anyone noticed.
the solution
01
Multi Source Consolidation
Consolidated validation across three source systems flowing through ADLS Gen2 to Azure SQL ODS.
03
Reconciliation and Join Logic
Engineered field to field mapping validation and join operation logic with comprehensive error detection and diagnostic reporting.
02
Automated Row and Field Validation
Implemented automated row count, schema, field to field, and distinctness validation checks across all three pipeline layers.
04
User Enablement and Adoption
Delivered client ready artifacts documenting findings, quality thresholds, and governance frameworks for ongoing operations.
Why It Matters
The provider now has a repeatable, automated way to catch data quality issues before they reach downstream reporting, replacing manual spot checks with systematic validation.