What are the best practices for error handling in ETL processes?

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In the realm of Business Intelligence (BI), Extract, Transform, Load (ETL) processes are crucial for data management and analytics. Effective error handling within these processes is essential to ensure data quality and reliability. ETL involves extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target data store or data warehouse. When errors occur, they can compromise data integrity, leading to faulty insights and business decisions. By adhering to best practices in error handling, you can minimize disruptions and maintain a robust BI environment.

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