Validate Zip Codes & Phone Numbers with iceDQ: Ensure Data Format Consistency
In today’s video, we show you how to validate zip codes and phone numbers in your data to ensure they conform to predefined standard formats using iceDQ. We demonstrate how to set up a validation rule to check if the postal code and phone numbers in the source data match the expected patterns.
We walk you through validating the zip code format (five digits, with an optional hyphen) and the phone number format (international code, area code, and number with optional separators like hyphens or dots).
Key Highlights
Pattern-Based Validation: Use regex patterns to validate zip codes and phone numbers in your data.
Zip Code Validation: Ensure the postal code is in the correct format (e.g., 12345 or 12345-6789).
Phone Number Validation: Verify that phone numbers follow the correct international format (e.g., +1 (123) 456-7890).
Comprehensive Data Checks: Validate multiple patterns across different columns, ensuring data consistency and accuracy.
With iceDQ, you can easily automate data validation and ensure that your ETL processes, data migration, and reporting are built on clean, consistent data.
Ready to automate your data validation?
Request a demo today and see how iceDQ can improve data quality and streamline your testing.
0:00 - Introduction to ZIP Code & Phone Number Validation
0:12 - Setting Up Data Source & Schema
0:33 - Previewing Data: ZIP Code & Phone Number Fields
0:52 - Defining ZIP Code Validation Rules
1:00 - Creating Phone Number Validation Rule
1:45 - Publishing & Executing the Validation Rule
2:19 - Investigating Validation Failures
2:50 - Summary of Regex Validation for Data Quality
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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