Data quality has been a critical and common practice employed across industries for many years. At the core, data quality encompasses six dimensions, including consistency, accuracy, validity, completeness, timeliness, and uniqueness. However, a significant challenge remains in streamlining these processes to prevent data management issues and enhance their utility for downstream analytics, data science, and machine learning. The session will delve into the six dimensions of data quality, detailing the specific techniques and features that enhance the Databricks Platform's functionality.

Talk By: Lara Rachidi, Solutions Architect, Databricks ; Liping Huang, Senior Solutions Architect, Databricks

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Data, Analytics, and AI Governance: https://dbricks.co/44gu3YU

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