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Answer: Insert each new CDC record and its corresponding operation type in real-time to a staging table, Use DML MERGE periodically to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
Delta tables capture all change events for a table since the initial load, which is crucial for analyzing trends, entity states at specific moments, or change frequency. Utilizing a MERGE statement is the most effective method for frequently and consistently merging data, as it combines multiple INSERT, UPDATE, and DELETE operations into a single atomic action. Reference: [Google Cloud Architecture](https://cloud.google.com/architecture/database-replication-to-bigquery-using-change-data-capture#overview_of_cdc_data_replication)
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When migrating on-premises data warehousing solutions to BigQuery, which two steps are recommended to optimize CDC performance and reduce compute overhead, ensuring near-real-time availability of source system changes in the BigQuery reporting table? (Choose two.)
A
Insert each new CDC record and its corresponding operation type in real-time to a staging table
B
Use DML MERGE periodically to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
C
Replicate each individual CDC record in real-time using DML INSERT, UPDATE, or DELETE operations directly on the reporting table
D
Periodically delete outdated records from the reporting table.
E
Insert each new CDC record and its corresponding operation type in real-time to the reporting table, and use a materialized view to expose only the latest version of each unique record