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Answer: Tracking and managing the movement of data across the data lifecycle to provide visibility into data flows.
Data lineage is the process of tracking and managing the movement of data across the data lifecycle. It plays a crucial role in data governance by providing visibility into data flows, origins, and transformations. In an Azure Databricks environment, data lineage can be used to enhance data governance by enabling organizations to understand data provenance, detect data quality issues, ensure compliance with regulatory requirements, and support data privacy initiatives. By leveraging data lineage, organizations can gain insights into data usage, identify potential data risks, and make informed decisions to improve data governance practices.
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In the context of data governance within an Azure Databricks environment, a company is looking to enhance its data governance practices by implementing a solution that provides visibility into data flows, origins, and transformations. The solution should help in understanding data provenance, detecting data quality issues, ensuring compliance with regulatory requirements, and supporting data privacy initiatives. Which of the following processes best addresses these requirements? (Choose one option)
A
Implementing data validation and cleansing processes to ensure data quality and accuracy.
B
Identifying and classifying data based on its sensitivity and criticality to manage data access.
C
Managing data access and permissions by implementing role-based access control (RBAC) to secure data.
D
Tracking and managing the movement of data across the data lifecycle to provide visibility into data flows.