How to Compare Transactional Data in Source with Aggregated Data in Target?

This video explores how to leverage reconciliation rules to compare transactional data with its corresponding aggregated version.

Data aggregation involves summarizing detailed data into a more concise format. However, discrepancies can arise during this process. This video demonstrates how iceDQ helps you verify the integrity of your aggregated data.

iceDQ’s reconciliation rules empower you to compare transactional data (source), containing detailed records, with its aggregated counterpart (target). The video showcases the process of creating a rule that establishes connections to both source and target tables, defines a join condition to match corresponding records and validates specific calculations within the aggregated data (e.g., sum of transaction amounts).

By successfully running the rule, you can identify discrepancies between the raw data and the aggregated values. This helps you maintain data integrity by spotting errors in the aggregation process and ensuring the aggregated data accurately reflects the underlying transactional details.

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