A common use case for the is_email_valid function would be cleaning or validating customer data in a BigQuery table.
Imagine you have a table of user information, including an email address column. You want to identify and potentially correct or remove invalid email addresses.
Scenario:
You have a table named users with columns like user_id, name, and email. You want to create a new table containing only users with valid email addresses.
Query:
SELECT *
FROM `your_project.your_dataset.users`
WHERE bigfunctions.your_region.is_email_valid(email);
Replace your_project, your_dataset, and your_region with your actual project ID, dataset ID and BigQuery region respectively (like bigfunctions.eu if your dataset is in EU multi-region).
This query uses the is_email_valid function to filter the users table, keeping only rows where the email column contains a valid email address according to the function's validation criteria.
Other Use Cases:
- Data Quality Reporting: Generate reports on the percentage of valid email addresses in your data. This helps track data quality and identify potential issues.
- Pre-processing for Marketing Campaigns: Ensure that your marketing emails are sent only to valid email addresses, reducing bounce rates and improving campaign effectiveness.
- Form Validation: Use the function as part of a data pipeline to validate email addresses submitted through online forms before storing them in your database.
- Lead Scoring: Assign higher scores to leads with valid email addresses, prioritizing them for sales outreach.
By incorporating the is_email_valid function into your BigQuery workflows, you can improve the accuracy and reliability of your data, leading to better decision-making and more effective business processes.