Contains Email Check Examples

The situation: The customers table stores the address used for transactional mail in contact_email. Rows where the column holds a phone number, a placeholder, or a note instead of an address silently break every downstream notification.

Check configuration

Payload

{
    "description": "Customer contact email must hold an email address.",
    "rule": "containsEmail",
    "fields": ["contact_email"],
    "container_id": 145,
    "coverage": 1,
    "filter": null,
    "properties": {},
    "tags": ["contact", "format"],
    "additional_metadata": {"jira": "DATA-7001"},
    "anomaly_message_field": null,
    "template_id": null,
    "status": "Active",
    "owner_id": 7,
    "default_anomaly_assignee_id": 12
}

Sample Data

What gets flagged

Customer C-002 has a phone number where the address should be. C-004 is NULL and passes without firing an anomaly. Coverage is 100%, so the failing row is reported as a Record Anomaly.

Flowchart

graph TD
    A["No filter, evaluate all rows"] --> B["Read contact_email as text"]
    B --> C{"Is value NULL?"}
    C -->|Yes| D["Row passes"]
    C -->|No| E{"Does it contain<br/>an email address?"}
    E -->|Yes| D
    E -->|No| F["Flag row.<br/>Record Anomaly per failing row."]

Equivalent SQL

-- Rows the Contains Email check would flag.
SELECT c.*
FROM customers c
WHERE c.contact_email IS NOT NULL
  AND c.contact_email NOT RLIKE '<email pattern>';

The situation: The notification_targets table holds one row per delivery channel. Only rows on the email channel must carry an address in target; rows for SMS or push hold phone numbers and device tokens and are out of scope.

Check configuration

Payload

{
    "description": "Email-channel targets must hold an email address.",
    "rule": "containsEmail",
    "fields": ["target"],
    "container_id": 512,
    "coverage": 1,
    "filter": "channel = 'email'",
    "properties": {},
    "tags": ["messaging", "format"],
    "additional_metadata": {"jira": "DATA-7042"},
    "anomaly_message_field": null,
    "template_id": null,
    "status": "Active",
    "owner_id": 7,
    "default_anomaly_assignee_id": 29
}

Sample Data (filtered to channel = 'email')

Why the filter matters

The filter runs before the evaluation, so SMS and push targets are never tested for an email address. Only email-channel rows are evaluated.

What gets flagged

T-502 holds the placeholder unknown, which contains no address, so it is the only row reported. Coverage is 100%, so the failure is reported as a Record Anomaly, and the message ends with the filter that scoped the evaluation.

Flowchart

graph TD
    A["Apply filter: channel = 'email'"] --> B["Read target as text"]
    B --> C{"Does it contain<br/>an email address?"}
    C -->|Yes| D["Row passes"]
    C -->|No| E["Flag row.<br/>Anomaly message ends with<br/>[filter: channel = 'email']"]

Equivalent SQL

-- Rows the check would flag on the email channel.
SELECT t.*
FROM notification_targets t
WHERE t.channel = 'email'
  AND t.target IS NOT NULL
  AND t.target NOT RLIKE '<email pattern>';

The situation: A legacy user_contact column mixes addresses with old free-text notes. A cleanup job is rewriting the bad rows, and a small fraction is expected to remain broken until it finishes, so the check tolerates up to 0.5% failures.

Check configuration

Payload

{
    "description": "Legacy contact column must hold an email address.",
    "rule": "containsEmail",
    "fields": ["user_contact"],
    "container_id": 733,
    "coverage": 0.995,
    "filter": null,
    "properties": {},
    "tags": ["legacy", "format"],
    "additional_metadata": {"jira": "DATA-7108"},
    "anomaly_message_field": null,
    "template_id": null,
    "status": "Active",
    "owner_id": 7,
    "default_anomaly_assignee_id": 41
}

Sample Data

What gets flagged

Users U-02 and U-03 still hold free-text notes. U-04 is NULL, so it passes without firing an anomaly but still counts in the scanned total. Only 50% of the rows pass, which is below the 99.5% coverage threshold, so the check reports a single Shape Anomaly for the dataset with a sample of the offending rows. Coverage below 100% does not produce Record Anomalies.

Flowchart

graph TD
    A["No filter, evaluate all rows"] --> B["Read user_contact as text"]
    B --> C{"Is value NULL?"}
    C -->|Yes| D["Row passes"]
    C -->|No| E{"Does it contain<br/>an email address?"}
    E -->|Yes| D
    E -->|No| F["Flag row.<br/>Passing rate falls below the 99.5%<br/>coverage, so one Shape Anomaly is reported."]

Equivalent SQL

-- Rows the check would flag.
SELECT u.*
FROM users u
WHERE u.user_contact IS NOT NULL
  AND u.user_contact NOT RLIKE '<email pattern>';