Let me clarify this with an example from my experience. In one of my healthcare projects, I designed an SSIS package to automate the ingestion and processing of formulary and drug data from multiple external sources. The ETL process started with extracting large flat files and API data, transforming it to match our internal schema, and then loading it into our SQL Server database.For data validation, I implemented several checks at the transformation stage. This included verifying required fields, checking for duplicate records, and validating data types and reference integrity. If any record failed validation, I redirected it to a separate error output, so it wouldn’t interrupt the main data flow.When it comes to error handling, I used SSIS event handlers extensively. For example, if a data flow task failed, the OnError event would log detailed error information into a dedicated logging table, including the row number and error description. For transient issues like network interruptions or API failures, I configured retry logic using script tasks and checkpoints, so the package could resume from the point of failure rather than starting over.Additionally, I set up email notifications to alert the support team if critical failures occurred. At the end of each run, I generated summary reports detailing the number of records processed, successful loads, and any errors encountered. This approach helped us maintain high data quality and ensured that any issues were quickly identified and resolved without impacting downstream systems.