During an ETL process, a testing team encounters performance issues. How should Test Data Management be adjusted to identify and resolve these issues?
- Conducting performance testing in a production-like environment, using production-sized datasets, considering data partitioning and indexing, utilizing load balancing techniques
- Increasing dataset size for more comprehensive testing, utilizing data compression techniques, testing on a less resource-intensive environment, relying on real-time data for testing
- Relying on simulated data for performance testing, testing only on a subset of data, avoiding complex transformations, using manual testing approaches
- Using smaller datasets for testing, optimizing queries, leveraging caching mechanisms, employing parallel processing for testing
To identify and resolve performance issues during an ETL process, Test Data Management should be adjusted by conducting performance testing in a production-like environment, using production-sized datasets, considering data partitioning and indexing, and utilizing load balancing techniques. This ensures a realistic performance assessment.
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