Let me clarify this with an example from my experience. In my previous role, I worked on a healthcare analytics project where we needed to integrate data from several external sources, including flat files, SQL databases, and Kafka topics. The challenge was to ensure that the data remained consistent and reliable throughout the process. To handle this, I designed an SSIS package that would first validate the schema and data quality of each incoming source before loading. For real-time data from Kafka, I set up checkpoints to avoid duplicate processing and used transactional control to ensure that only complete and accurate batches were committed. When merging data from different sources, I implemented business rules to resolve conflicts and performed data reconciliation checks after each load. Additionally, I scheduled automated jobs to run data integrity checks and generate exception reports, so any anomalies could be addressed quickly. This approach helped maintain a high level of data consistency and reliability, even when dealing with frequent updates and multiple data formats.