Scenario: Your company is dealing with a massive amount of data, and performance issues are starting to arise. As a data engineer, how would you evaluate whether denormalization is a suitable solution to improve performance?
- Analyze query patterns and workload characteristics to identify opportunities for denormalization
- Consider sharding the database to distribute the workload evenly and scale horizontally
- Implement indexing and partitioning strategies to optimize query performance
- Stick to normalization principles to ensure data integrity and consistency, even at the expense of performance
To evaluate whether denormalization is suitable for improving performance in a data-intensive environment, it's essential to analyze query patterns and workload characteristics. By understanding how data is accessed and processed, you can identify opportunities to denormalize certain structures and optimize query performance without sacrificing data integrity.
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