In this video, we demonstrate data reconciliation using iceDQ to test the accuracy and consistency of reference data. We create a reconciliation rule to compare the “Phone Number Type” table generated by our ETL process (source data) with the Master reference data in a separate reference database.
The reconciliation rule defines the parameters for the comparison. We establish connections to both the source and target databases, specifying the relevant tables (“Phone Number Type”). We then configure the rule to compare specific data points (attributes) between the two tables.
Our primary focus is to test if the “name” values (value data) for each phone number type (entity) match exactly between the source and reference data. Additionally, we include the “modified date” attribute in the comparison to ensure data consistency.
By running the reconciliation rule, we can identify any discrepancies between the source and reference data. In this example, the rule identified mismatched “modified dates” while the “name” values matched perfectly.
This data reconciliation process using reconciliation rules helps ensure the integrity of our reference data, guaranteeing its accuracy and homogeneity.
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The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
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