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Answer: CDF captures changes at the row level, enabling precise tracking and application of updates and deletes across various data processing stages.
Change Data Feed (CDF) is crucial in Lakehouse architectures as it captures detailed change events at the row level. This capability allows for precise tracking of updates and deletes, ensuring that downstream processes and data consumers receive the most current and accurate data. For instance, in a financial application, CDF ensures that ledger updates are accurately reflected in all related reports and analytics without manual reconciliation efforts.
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Explain how Change Data Feed (CDF) addresses past difficulties in propagating updates and deletes within Lakehouse architecture. Provide a detailed scenario where CDF is essential for maintaining data integrity and consistency.
A
CDF primarily improves data retrieval speed without affecting update and delete operations.
B
CDF simplifies data storage management by automatically handling data versioning.
C
CDF captures changes at the row level, enabling precise tracking and application of updates and deletes across various data processing stages.
D
CDF focuses on enhancing data security by restricting access to change logs.