Summary

The article outlines Halodoc's comprehensive approach to data validation within a Lakehouse architecture, emphasizing the importance of data accuracy and reliability. It describes a multi-layered validation strategy that employs AI to enhance data quality checks at various stages of the data pipeline. The validation layers include checks for data consistency, structural correctness, business correctness, and reconciliation, ensuring that data remains trustworthy throughout its journey. The implementation of this strategy has led to reduced data incidents and increased trust among analytics and product teams.

Data Problems Addressed

Technologies Referenced