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Answer: Model the data in a single table with both valid-time and transaction-time columns, partitioning and clustering to optimize bi-temporal queries.
Modeling the data in a single table with both valid-time and transaction-time columns (Option D) is the most efficient approach for bi-temporal queries in a lakehouse architecture. This method allows for partitioning and clustering based on these columns, significantly optimizing query performance. It simplifies data management, reduces redundancy, and fully leverages the lakehouse capabilities. Other options, such as using separate tables (Option C) or a traditional RDBMS (Option A), introduce complexity or inefficiency. Implementing custom versioning logic (Option B) may not fully utilize the lakehouse's features and can be cumbersome to maintain. Therefore, Option D is the recommended strategy for this scenario.
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How should you model financial data requiring bi-temporal querying capabilities (valid time and transaction time) in a lakehouse architecture to optimize query performance?
A
Use a traditional RDBMS for bi-temporal queries and periodically archive data to the lakehouse.
B
Implement custom versioning logic within the application layer, bypassing lakehouse capabilities.
C
Create separate tables for valid time and transaction time, joining them for bi-temporal queries.
D
Model the data in a single table with both valid-time and transaction-time columns, partitioning and clustering to optimize bi-temporal queries.
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