Validate Date Formats in Your Tables with iceDQ: Ensure Data Consistency
In today’s video, we demonstrate how to validate date values in your database table to ensure they are in the correct format using iceDQ. Specifically, we validate date columns like sell start date, sell end date, and discontinued date in the AdventureWorks database.
We walk you through the process of creating a validation rule to check if the date columns follow a custom format (in this case, yyyy-MM-dd HH:mm:ss.S), and how to pinpoint any discrepancies by including the product ID in the validation check.
Key Highlights
Date Format Validation: Validate that your date columns are in the expected format (custom or pre-existing).
Custom Date Format: Learn how to create a custom format for your date columns to match specific requirements.
Efficient Data Validation: Use iceDQ’s validation rules to automate checks and pinpoint specific discrepancies in your data.
Quick Issue Identification: Add key identifiers like product ID to help quickly identify where mismatches occur.
With iceDQ, you can automate data validation, ensuring your date columns follow the correct format and improve data consistency across your ETL processes and data migrations.
Ready to automate your data validation?
Request a demo today to see how iceDQ can help you ensure data integrity and streamline your data testing.
00:00 - Introduction
00:13 - Setting Up the Validation Rule
00:41 - Previewing Date Columns
01:00 - Creating Custom Date Format Checks
02:06 - Applying Best Practices
02:57 - Publishing and Executing the Rule
03:16 - Results and Conclusion
Request a Demo: https://icedq.com/request-a-demo
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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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