Automating Data Validation with iceDQ: Validate CSV Data for Accuracy & Compliance
In this video, we demonstrate how iceDQ can be used to validate data in a CSV file, ensuring it meets your business rules and compliance standards. Specifically, we validate the email column for null values and check if the email format aligns with a predefined pattern.
Key Highlights
CSV File Validation: Learn how to set up and run a validation rule on a CSV file to check for null values and invalid email formats.
Flexible Rule Creation: Create custom rules to verify data integrity, including pattern matching and ensuring the correct format for email addresses.
Detailed Results: Get insights into failed validations by examining specific records and why they failed.
Easy Investigation: Access failure details by clicking on individual records to view the exact errors (e.g., invalid patterns or missing values).
With iceDQ, you can automate data validation across your entire ETL process, ensuring that data migration, ETL testing, Big Data, and BI reports remain accurate and reliable.
00:00 - Introduction
00:12 - Validation Goal
00:27 - Creating a Rule in iceDQ
00:36 - Configuring the Source File
01:26 - Adding Email Pattern Check
02:07 - Adding Null Value Check & Customer Column
02:53 - Reviewing Validation Results
Ready to automate your data validation?
Visit iceDQ.com today to see how you can streamline your data testing and ensure higher-quality, compliant data.
Request a Demo today: 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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