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Quickstart

Learn Tabular in 5 minutes with a complete validation pipeline.

Step 1: Load the Extension​

LOAD anofox_tabular;

Step 2: Create Sample Customer Data​

CREATE TABLE customers AS
SELECT
    1 as customer_id, 'john.doe@example.com' as email, 'DE123456789' as vat_id, 1500.50 as amount
UNION ALL
SELECT 2, 'invalid.email@', 'FR12345678', 2000.00
UNION ALL
SELECT 3, 'jane@company.org', 'INVALID', -50.00
UNION ALL
SELECT 4, 'bob+test@domain.co.uk', 'GB123456789', 3500.75;

Step 3: Validate Emails​

SELECT
    customer_id,
    email,
    anofox_email_validate(email, 'regex') as regex_valid,
    anofox_email_validate(email, 'dns') as dns_valid
FROM customers;

Output:

customer_id | email                     | regex_valid | dns_valid
1           | john.doe@example.com      | true        | true
2           | invalid.email@            | false       | false
3           | jane@company.org          | true        | true
4           | bob+test@domain.co.uk     | true        | true

Step 4: Validate VAT IDs​

SELECT
    customer_id,
    vat_id,
    anofox_vat_is_valid(vat_id) as vat_valid,
    anofox_vat_get_country(vat_id) as country
FROM customers;

Output:

customer_id | vat_id        | vat_valid | country
1           | DE123456789   | true      | DE
2           | FR12345678    | false     | FR
3           | INVALID       | false     | null
4           | GB123456789   | true      | GB

Step 5: Validate Monetary Amounts​

SELECT
    customer_id,
    amount,
    anofox_money_is_positive(amount) as positive,
    anofox_money_is_valid_amount(amount, 0.01, 9999999.99) as in_range
FROM customers;

Output:

customer_id | amount  | positive | in_range
1           | 1500.50 | true     | true
2           | 2000.00 | true     | true
3           | -50.00  | false    | false
4           | 3500.75 | true     | true

Step 6: Combine All Validations​

CREATE TABLE customers_validated AS
SELECT
    customer_id,
    email,
    vat_id,
    amount,
    CASE WHEN anofox_email_validate(email, 'dns') IS TRUE THEN TRUE ELSE FALSE END as email_valid,
    CASE WHEN anofox_vat_is_valid(vat_id) IS TRUE THEN TRUE ELSE FALSE END as vat_valid,
    CASE WHEN anofox_money_is_positive(amount) IS TRUE THEN TRUE ELSE FALSE END as amount_valid,
    CASE
        WHEN anofox_email_validate(email, 'dns') IS TRUE
         AND anofox_vat_is_valid(vat_id) IS TRUE
         AND anofox_money_is_positive(amount) IS TRUE
        THEN 'PASS'
        ELSE 'FAIL'
    END as validation_status
FROM customers;

SELECT * FROM customers_validated WHERE validation_status = 'PASS';

Output:

customer_id | email                  | vat_id      | amount  | email_valid | vat_valid | amount_valid | validation_status
1           | john.doe@example.com   | DE123456789 | 1500.50 | true        | true      | true         | PASS
4           | bob+test@domain.co.uk  | GB123456789 | 3500.75 | true        | true      | true         | PASS

Step 7: Detect Anomalies​

-- Add some outlier-prone values
CREATE TABLE sales_transactions AS
SELECT
    1 as transaction_id, 100.00 as amount
UNION ALL
SELECT 2, 105.00
UNION ALL
SELECT 3, 110.00
UNION ALL
SELECT 4, 5000.00  -- Obvious outlier
UNION ALL
SELECT 5, 102.00;

-- Detect anomalies using Z-Score
SELECT
    transaction_id,
    amount,
    anofox_zscore_anomaly(amount, 3.0) OVER () as is_anomaly
FROM sales_transactions;

Output:

transaction_id | amount   | is_anomaly
1              | 100.00   | false
2              | 105.00   | false
3              | 110.00   | false
4              | 5000.00  | true
5              | 102.00   | false

Step 8: Calculate Data Quality Metrics​

SELECT
    COUNT(*) as total_records,
    COUNT(*) FILTER (WHERE email IS NOT NULL) as email_non_null,
    COUNT(DISTINCT email) as distinct_emails,
    ROUND(COUNT(*) FILTER (WHERE email IS NOT NULL) * 100.0 / COUNT(*), 2) as completeness_pct
FROM customers;

Output:

total_records | email_non_null | distinct_emails | completeness_pct
4             | 4              | 4               | 100.00

What You Learned​

✅ Email validation (regex, DNS checks)
✅ VAT ID validation (country-specific rules)
✅ Monetary amount validation
✅ Anomaly detection (Z-Score)
✅ Data quality metrics

Next Steps​

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