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Answer: Implement a versioned approach where each record update duplicates the data row with new transaction and valid times, using Delta Lake‘s time travel for querying specific versions.
By implementing a versioned approach, each record update will create a new version of the data row with updated transaction and valid times. This allows for querying both transaction time and valid time without duplicating the entire dataset. Delta Lake‘s time travel feature enables querying specific versions of the data, making it efficient to retrieve historical data based on transaction time and valid time. This approach ensures data integrity and consistency by keeping track of changes over time, providing a comprehensive audit trail for regulatory compliance. Using a single table with separate columns for transaction time and valid time simplifies data management and querying, as all relevant information is stored in one place. Overall, this approach optimizes data storage and retrieval in a lakehouse environment, meeting the requirements for bi-temporal modeling in regulatory compliance for financial data.
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How can you implement a bi-temporal model in a lakehouse for financial data under regulatory compliance, enabling queries on both transaction and valid times without data duplication?
A
Store transaction logs in an append-only table, with another table capturing the state of data at specific valid times, facilitating bi-temporal queries through joins.
B
Create separate columns for transaction time and valid time in a single table, with partitions based on transaction time and clustering on valid time.
C
Implement a versioned approach where each record update duplicates the data row with new transaction and valid times, using Delta Lake‘s time travel for querying specific versions.
D
Utilize two parallel tables, one for transaction time and another for valid time, with a process to synchronize updates across both.