Data reconciliation in data replication processes involves addressing complex technical challenges such as handling data type mismatches between databases like MySQL and PostgreSQL, managing collation issues during migrations from Oracle to Snowflake to ensure consistent text comparison, and optimizing replication pipelines to mitigate performance bottlenecks from large data volumes. Organizations often struggle to test these pipelines effectively, delaying validation until failures occur. This part of a three-part series explores five key technical challenges—speed, efficiency, detail, data types, and collations—and proposes three categories of solutions: manual, rule-based, and data diffs. The series aims to provide a comprehensive understanding of data reconciliation, with previous parts covering use cases and challenges, and future parts set to discuss best practices like selecting validation metrics and automating data quality testing.