What role does data cleansing play in the ETL process, and why is it crucial for testing?
- Data cleansing ensures that data is accurate, consistent, and conforms to predefined standards, enhancing the quality of data analysis and decision-making.
- Data cleansing involves compressing data to reduce storage requirements and improve data retrieval performance.
- Data cleansing is primarily focused on encrypting sensitive data to protect it from unauthorized access during the ETL process.
- Data cleansing is solely responsible for transforming raw data into actionable insights through statistical analysis.
Data cleansing is a critical step in the ETL process, where data is cleaned, standardized, and validated to ensure accuracy and consistency. This process improves the quality of data analysis and decision-making by providing reliable and trustworthy data. Testing data cleansing functionalities ensures that the ETL process effectively handles data quality issues and produces reliable results.
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