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Answer: CDF efficiently captures and propagates row-level changes to the data, enabling real-time analytics and reporting without the need for full data scans or complex ETL processes.
Change Data Feed (CDF) in Azure Databricks Lakehouse architecture is designed to efficiently manage and propagate row-level changes to data, such as updates and deletes. This capability is crucial for scenarios requiring real-time analytics, like monitoring customer transactions across different regions. By capturing only the changes, CDF eliminates the need for full data scans or complex ETL processes, ensuring that analytics and reporting are based on the most current data. This makes CDF the optimal solution for the multinational corporation's needs, as it supports real-time data changes without unnecessary storage costs or complexity.
Author: LeetQuiz Editorial Team
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In the context of Azure Databricks Lakehouse architecture, Change Data Feed (CDF) plays a pivotal role in managing data changes efficiently. Considering a scenario where a multinational corporation requires real-time analytics on customer transactions that are frequently updated and deleted across different regions, which of the following best describes how CDF addresses the challenges of propagating updates and deletes, and why it would be the optimal solution in this scenario? (Choose one correct answer)
A
CDF does not support real-time data changes, making it unsuitable for scenarios requiring immediate analytics on updated or deleted data.
B
CDF captures and propagates only the metadata of changes without affecting the actual data, thus ensuring data remains static for analytics.
C
CDF creates a full snapshot of the data for every change, significantly increasing storage costs and complexity without providing real-time change propagation.
D
CDF efficiently captures and propagates row-level changes to the data, enabling real-time analytics and reporting without the need for full data scans or complex ETL processes.
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