Practical Techniques for Applying Data Quality in the Lakehouse with Databricks

Explore practical techniques for applying data quality in the Lakehouse with Databricks in this 41-minute conference talk. Dive into the six dimensions of data quality: consistency, accuracy, validity, completeness, timeliness, and uniqueness. Discover how to streamline data management processes to prevent issues and enhance utility for downstream analytics, data science, and machine learning. Solutions Architects Lara Rachidi and Liping Huang detail specific techniques and features that improve the Databricks Platform's functionality. Gain insights into data, analytics, and AI governance while learning how to effectively implement data quality practices across industries using the Databricks Lakehouse architecture.