Reconcile Data Between SQL Server and Snowflake Using iceDQ: Data Validation Across Databases

In today’s video, we show you how to compare data between two different database sources: SQL Server and Snowflake using iceDQ. Specifically, we walk you through the process of setting up a reconciliation rule to ensure that the customer data from both sources matches exactly.

We demonstrate how to reconcile dim customer data between SQL Server and Snowflake, address data type discrepancies, and perform data validation to identify mismatches in columns such as gender, phone number, and yearly income.

Key Highlights

* Reconciliation Rule Setup: Learn how to set up a reconciliation rule to compare data from different database sources.
* Data Type Handling: Handle discrepancies in data types (e.g., phone numbers and yearly income) between SQL Server and Snowflake using Groovy functions.
* Data Validation: Validate whether customer data like first name, last name, gender, and phone numbers match between the two databases.
* Discrepancy Investigation: Explore how to investigate and resolve data mismatches, such as gender mismatches and inconsistent phone number formats.

With iceDQ, you can automate data validation across your databases, ensuring accurate data migration and reconciliation between different systems.

Ready to automate your data reconciliation?
Request a demo today and see how iceDQ can streamline your ETL processes, data migrations, and data monitoring.

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About iceDQ: Ensuring Reliable Data From Development to Production with iceDQ.

iceDQ is a one-stop platform for data reliability with unified data testing, monitoring, and observability. Large banks, insurance, healthcare, and other enterprises rely on iceDQ in both development and production environments, ensuring data reliability and robust processes.

Streamlined Data Testing in Development: iceDQ is used to automate data migration testing, ETL data pipeline testing, big data lake testing, BI report testing, and more. It helps identify and fix data issues early in the data development lifecycle.

Proactive Monitoring and Observability in Production: iceDQ is used by operations to establish checks and controls for their data pipelines, and the AI-based observability engine ensures anomalies are detected and incidents are reported.
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#SQLServer #Snowflake #DataValidation #ETLTesting #DataReconciliation #iceDQ