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Contains Credit Card

Use the containsCreditCard rule when you need to ensure that sensitive payment fields actually contain valid credit card numbers.

What is Contains Credit Card?

Think of Contains Credit Card as a sanity check for payment data.

Just like a cashier won’t accept a payment without a card number, this rule ensures that a selected field contains a valid credit card number — not empty values, placeholders, or incorrect data.

In simple terms: It verifies that credit card fields truly contain credit card numbers.

Add Contains Credit Card Check

Use the Contains Credit Card check to validate payment-related fields and detect records where the credit card number is missing or invalid.

What Does Contains Credit Card Do?

It helps you answer questions like:

  • “Do all completed orders actually have a credit card number?”
  • “Are payment records missing sensitive fields?”
  • “Did upstream systems send incomplete payment data?”

In short: It flags records where a credit card number is expected but missing or invalid.

How Does Contains Credit Card Work?

Step 1: Select the Field

Choose a single field that should contain a credit card number (e.g., CARD_NUMBER).

Step 2: Pattern Validation

Qualytics checks whether each value matches known credit card formats (continuous digits or hyphen-separated patterns).

Step 3: Anomaly Detection

If a record does not contain a valid credit card number, it is marked anomalous.

Step 4: Review Results

You can review exactly which records failed and why.

Real-Life Example: E-commerce Orders

The Situation

An e-commerce company stores order data in the ECOMMERCE_ORDERS table.

Each successful order is expected to include a credit card number in the CARD_NUMBER field.

This data is critical for:

  • Payment reconciliation
  • Fraud investigation
  • Compliance audits

The Problem

Before this check was configured, the data team had to manually review order records to verify whether credit card numbers were present.

This manual process:

  • Took significant time during each review
  • Did not scale as order volume increased
  • Still risked missing incomplete or invalid payment records

The Solution

The team implemented the Contains Credit Card check on the CARD_NUMBER field.

With this check in place, Qualytics automatically validates every record during each scan and flags any order where the credit card number is missing or invalid—removing the need for manual review.

What the Check Detected

During the scan, Qualytics identified 26 anomalous records where the CARD_NUMBER field did not contain a valid credit card number, even though the orders were marked as completed.

Output

CARD_NUMBER CUSTOMER_EMAIL PRODUCT_ID QUANTITY ORDER_DATE
Missing customer056@example.com P115 1 2022-02-01
Missing customer052@example.com P111 -1 2022-02-01
Missing customer048@example.com P107 3 2022-01-01
Missing customer036@example.com P115 1 2022-02-01
Missing customer028@example.com P107 3 2022-02-01
Missing customer020@example.com P119 5 2022-02-01
Missing customer016@example.com P115 1 2022-02-01
Missing customer012@example.com P111 2 2022-02-01
Missing customer008@example.com P107 3 2022-02-01
Missing customer004@example.com P103 4 2022-02-01

output

Anomaly Detected

  • Rule Applied: Contains Credit Card
  • Field: CARD_NUMBER
  • Anomalous Records: 26
  • Violation: Credit card number not found

Violation Message Example: In CARD_NUMBER, 26.00% of filtered records (26) do not contain credit card numbers.

Why This Matters

Without this check:

  • Payments cannot be reliably audited
  • Fraud investigations lack critical data
  • Compliance requirements may fail
  • Downstream billing systems may break

The Outcome

After enabling the Contains Credit Card check:

  • The team identified faulty upstream ingestion logic
  • Missing card numbers were traced to a failed payment gateway response
  • Incomplete payment records were blocked
  • Data quality and compliance confidence improved

When Should You Use Contains Credit Card?

Use this rule when you:

  • Validate payment or transaction tables
  • Enforce PCI or financial compliance
  • Monitor upstream payment integrations
  • Prevent incomplete payment records

Key Takeaway

Contains Credit Card acts as a guardrail for payment data. If a system says “payment received”, this rule ensures the data proves it.

Field Scope

Single: The rule evaluates a single specified field.

Accepted Types

Type
String

General Properties

Name Supported
Filter
Allows the targeting of specific data based on conditions
Coverage Customization
Allows adjusting the percentage of records that must meet the rule's conditions

The filter allows you to define a subset of data upon which the rule will operate.

It requires a valid Spark SQL expression that determines the criteria rows in the DataFrame should meet. This means the expression specifies which rows the DataFrame should include based on those criteria. Since it's applied directly to the Spark DataFrame, traditional SQL constructs like WHERE clauses are not supported.

Examples

Direct Conditions

Simply specify the condition you want to be met.

Correct usage" collapsible="true 
O_TOTALPRICE > 1000

C_MKTSEGMENT = 'BUILDING'
Incorrect usage" collapsible="true 
WHERE O_TOTALPRICE > 1000

WHERE C_MKTSEGMENT = 'BUILDING'

Combining Conditions

Combine multiple conditions using logical operators like AND and OR.

Correct usage" collapsible="true 
O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'

(L_SHIPDATE = '1998-09-02' OR L_RECEIPTDATE = '1998-09-01') AND L_RETURNFLAG = 'R'
Incorrect usage" collapsible="true 
WHERE O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'

O_TOTALPRICE > 1000, O_ORDERSTATUS = 'O'

Utilizing Functions

Leverage Spark SQL functions to refine and enhance your conditions.

Correct usage" collapsible="true 
RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - INSTR('-', O_ORDERPRIORITY)
) = 'URGENT'

LEVENSHTEIN(C_NAME, 'Supplier#000000001') < 7
Incorrect usage" collapsible="true 
RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - CHARINDEX('-', O_ORDERPRIORITY)
) = 'URGENT'

EDITDISTANCE(C_NAME, 'Supplier#000000001') < 7

Using scan-time variables

To refer to the current dataframe being analyzed, use the reserved dynamic variable {{ _qualytics_self }}.

Correct usage" collapsible="true 
O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM {{ _qualytics_self }}
    WHERE O_TOTALPRICE > 1000
)
Incorrect usage" collapsible="true 
O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM ORDERS
    WHERE O_TOTALPRICE > 1000
)

While subqueries can be useful, their application within filters in our context has limitations. For example, directly referencing other containers or the broader target container in such subqueries is not supported. Attempting to do so will result in an error.

Important Note on {{ _qualytics_self }}

The {{ _qualytics_self }} keyword refers to the dataframe that's currently under examination. In the context of a full scan, this variable represents the entire target container. However, during incremental scans, it only reflects a subset of the target container, capturing just the incremental data. It's crucial to recognize that in such scenarios, using {{ _qualytics_self }} may not encompass all entries from the target container.

Anomaly Types

Type Supported
Record
Flag inconsistencies at the row level
Shape
Flag inconsistencies in the overall patterns and distributions of a field

Example

Objective: Ensure that every O_PAYMENT_DETAILS in the ORDERS table contains a credit card number to confirm the payment method used for each order.

Sample Data

O_ORDERKEY O_PAYMENT_DETAILS
1 {"date": "2023-09-25", "amount": 250.50, "credit_card": "5105105105105100"}
2 {"date": "2023-09-25", "amount": 150.75, "credit_card": "ABC12345XYZ"}
3 {"date": "2023-09-25", "amount": 200.00, "credit_card": "4111-1111-1111-1111"} 
{
    "description": "Ensure that every O_PAYMENT_DETAILS in the ORDERS table contains a credit card number to confirm the payment method used for each order",
    "coverage": 1,
    "properties": {},
    "tags": [],
    "fields": ["C_CCN_JSON"],
    "additional_metadata": {"key 1": "value 1", "key 2": "value 2"},
    "rule": "containsCreditCard",
    "container_id": {container_id},
    "template_id": {template_id},
    "filter": "1=1"
}

Anomaly Explanation

In the sample data above, the entry with O_ORDERKEY 2 violates the rule as the O_PAYMENT_DETAILS does not contain a credit card number, indicating an incomplete order record.

graph TD
A[Start] --> B[Retrieve O_PAYMENT_DETAILS]
B --> C{Contains Credit Card Number?}
C -->|Yes| D[Move to Next Record/End]
C -->|No| E[Mark as Anomalous]
E --> D
-- An illustrative SQL query to identify order records that don't contain a credit card number in the payment details.
select
    o_orderkey,
    o_payment_details
from orders
where
    not (regexp_like(o_payment_details, '[0-9]{16}'))
    or not (regexp_like(o_payment_details, '\d{4}-\d{4}-\d{4}-\d{4}'))

Potential Violation Messages

Record Anomaly

The O_PAYMENT_DETAILS value of {"date": "2023-09-25", "amount": 150.75, "credit_card": "ABC12345XYZ"} does not contain a credit card number.

Shape Anomaly

In O_PAYMENT_DETAILS, 33.33% of 3 order records (1) do not contain a credit card number.