A financial institution needs to store transaction records of millions of customers securely while ensuring quick access to historical data. How could partitioning be utilized in the relational schema design to meet these requirements?
- Composite partitioning based on customer ID and transaction date
- Hash partitioning based on customer ID
- List partitioning based on transaction type
- Range partitioning based on transaction date
In this case, range partitioning based on transaction date could be employed to efficiently store and retrieve historical transaction data. Range partitioning organizes data based on specified ranges, making it easier to manage large datasets and optimize query performance for time-based queries.
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