Data reconciliation: Technical best practices

The text discusses the intricacies of data reconciliation, emphasizing the importance of technical best practices often overlooked in favor of non-technical advice found online. It highlights the challenges faced by data engineers, especially those establishing new data infrastructures or joining projects mid-course. The final installment of a three-part series explores three critical technical best practices: selecting validation metrics, managing resources efficiently, and automating data quality testing. It differentiates between data reconciliation during replication testing and migration, noting that replication requires ongoing validation to ensure data consistency between databases, while migration involves intricate validation due to structural changes between different database systems. Key techniques like validation tools, data integrity checks, schema matching, and data type conversion checks are essential for maintaining data accuracy and integrity throughout these processes.

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