Scenario: A data warehouse project is facing delays due to data quality issues during the transformation phase of the ETL process. How would you approach data quality assessment and cleansing to ensure the success of the project?
- Data aggregation techniques, data sampling methods, data anonymization approaches, data synchronization mechanisms
- Data archiving policies, data validation procedures, data modeling techniques, data synchronization strategies
- Data encryption techniques, data masking approaches, data anonymization methods, data compression techniques
- Data profiling techniques, data quality dimensions assessment, outlier detection methods, data deduplication strategies
To address data quality issues during the transformation phase of the ETL process, it's essential to employ data profiling techniques, assess data quality dimensions, detect outliers, and implement data deduplication strategies. These approaches ensure that the data in the warehouse is accurate and reliable, contributing to the project's success.
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