gcp-pipeline-transform 1.0.29

Transform library for GCP data pipelines (dbt macros)

gcp-pipeline-transform

SQL library - dbt macros for audit columns and PII masking.

NO Apache Beam or Airflow dependencies.

---

Architecture

                      GCP-PIPELINE-TRANSFORM
                      ──────────────────────

  ┌─────────────────────────────────────────────────────────────────┐
  │                     SQL LAYER                                    │
  │                                                                  │
  │  ┌─────────────────────────────────────────────────────────┐    │
  │  │                   dbt Macros                             │    │
  │  │                                                          │    │
  │  │  ┌─────────────────┐    ┌─────────────────┐             │    │
  │  │  │ Audit Columns   │    │  PII Masking    │             │    │
  │  │  │                 │    │                 │             │    │
  │  │  │ • run_id        │    │ • mask_pii()    │             │    │
  │  │  │ • source_file   │    │ • mask_full()   │             │    │
  │  │  │ • processed_    │    │ • mask_partial  │             │    │
  │  │  │   timestamp     │    │ • Configurable  │             │    │
  │  │  └─────────────────┘    └─────────────────┘             │    │
  │  │                                                          │    │
  │  └─────────────────────────────────────────────────────────┘    │
  │                              │                                   │
  │                              ▼                                   │
  │  ┌─────────────────────────────────────────────────────────┐    │
  │  │                   SQL Templates                          │    │
  │  │                                                          │    │
  │  │  • Staging models (clean raw data)                      │    │
  │  │  • FDP models (business transformations)                │    │
  │  │  • Quality checks (dbt tests)                           │    │
  │  │                                                          │    │
  │  └─────────────────────────────────────────────────────────┘    │
  │                                                                  │
  └─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
                    Used by: bigquery-to-mapped-product, fdp-to-consumable-product

---

Transformation Flow

  BigQuery ODP                   dbt                      BigQuery FDP
  ────────────                   ───                      ────────────

  Raw tables      ┌───────────────────────────────────┐
  (from Beam)     │                                   │
       │          │  1. Staging Models                │
       └─────────►│     {{ source('odp', 'table') }}  │
                  │     • Clean data types            │
                  │     • Apply naming conventions    │
                  │                                   │
                  │  2. Add Audit Columns             │
                  │     {{ add_audit_columns() }}     │
                  │     • run_id                      │
                  │     • processed_timestamp         │
                  │                                   │
                  │  3. Apply PII Masking             │────► FDP Tables
                  │     {{ mask_pii(col, type) }}     │
                  │     {{ mask_full(column) }}       │
                  │                                   │
                  │  4. Business Logic                │
                  │     • JOINs (multi-source → 1)              │
                  │     • MAPs (1:1 column rename)              │
                  │                                   │
                  └───────────────────────────────────┘

---

Macros

add_audit_columns

Adds standard audit columns to every FDP table.

-- Usage in dbt model
SELECT
    customer_id,
    first_name,
    last_name,
    {{ add_audit_columns() }}
FROM {{ ref('stg_customers') }}

-- Output columns added:
--   run_id STRING
--   processed_timestamp TIMESTAMP
--   source_file STRING

mask_pii

Metadata-driven PII masking. Routes to the correct strategy based on pii_type.

-- Usage
SELECT
    customer_id,
    {{ mask_pii('ssn', 'SSN') }} as ssn_masked,
    {{ mask_pii('email', 'EMAIL') }} as email_masked
FROM {{ ref('stg_customers') }}

-- SSN Input:  123-45-6789
-- SSN Output: XXX-XX-6789
-- Email Input:  [e-mail]
-- Email Output: ****@example.com

mask_full

Complete masking — replaces all characters with a mask character.

-- Usage
SELECT
    customer_id,
    {{ mask_full('secret_key') }} as key_masked
FROM {{ ref('stg_customers') }}

-- Input:  ABC123
-- Output: ******

---

PII Masking Configuration

PII masking is configurable per schema. Define which fields to mask in the entity schema:

# In deployment schema definition
from gcp_pipeline_core.schema import EntitySchema, SchemaField

CustomerSchema = EntitySchema(
    entity_name="customers",
    system_id="Generic",
    fields=[
        SchemaField(name="customer_id", field_type="STRING", required=True),
        SchemaField(name="ssn", field_type="STRING", is_pii=True),
        SchemaField(name="dob", field_type="DATE", is_pii=True),
    ],
    primary_key=["customer_id"]
)

---

Key Findings

1. Standardized Audit Macros

• add_audit_columns(): Ensures consistent lineage tracking across all models by adding run_id, processed_timestamp, and source_file.
• apply_audit_columns(): Utility to retroactively add audit columns to existing tables.

2. Metadata-Driven PII Masking

• Generic Masking Engine: Focuses on the shape of masking rather than the type of data.
• Core Strategies:
  • mask_full(): Complete masking based on field length.
  • mask_partial_last4(): Preserves utility (last 4) while protecting privacy.
  • mask_redacted(): Replaces sensitive values with a constant "REDACTED" label.
• Metadata-Powered: Selection is driven by pii_type in the schema metadata (e.g., pii_type: PARTIAL), ensuring the library doesn't make blind assumptions about data content.
• Environment-Aware: Automatically adjusts depth (Full vs. Partial vs. None) based on the target environment (Prod vs. Staging vs. Dev).

3. Configurable Data Enrichment

• Generic Macro: apply_enrichment(rules)
• Rule Types:
  • DATE_PARTS: Automatically extracts year, month, day, and day name.
  • BUCKET: Categorizes numeric values into ranges (e.g., credit scores).
  • LOOKUP: Maps legacy codes to human-readable statuses.
  • EXPRESSION: Applies custom SQL expressions for complex logic.
• Config-Driven: Enrichment is defined via metadata, keeping the library code generic and reusable across systems.

4. Data Safety & Validation

• validate_no_pii_in_export: Safety macro that checks for unmasked PII patterns before data export, preventing accidental exposure of sensitive information.

---

Governance & Compliance

• SQL Only: This library is strictly for dbt/SQL logic. NO Python dependencies (Beam/Airflow) allowed.
• Consistency: All transformation models must use add_audit_columns() to maintain data lineage.
• Privacy: High-risk PII fields (SSN, etc.) MUST be masked using the provided macros in all non-production exports.

---

Directory Structure

gcp-pipeline-transform/
└── dbt_shared/
    └── macros/
        ├── audit_columns.sql        # add_audit_columns(), apply_audit_columns()
        ├── pii_masking.sql          # mask_pii(), mask_full(), mask_partial_last4(), mask_email(), etc.
        ├── data_quality_check.sql   # row_count_check(), etc.
        └── enrichment.sql           # apply_enrichment(rules)

---

Usage in Deployment

Reference in your deployment's dbt project:

# dbt_project.yml
name: 'generic_transformation'

# Reference shared macros
packages:
  - local: ../../gcp-pipeline-libraries/gcp-pipeline-transform/dbt_shared

Or copy macros to your project:

cp -r gcp-pipeline-libraries/gcp-pipeline-transform/dbt_shared/macros \
      deployments/bigquery-to-mapped-product/dbt/macros/shared/

---

Run Tests

Run dbt macro unit tests:

cd gcp-pipeline-libraries/gcp-pipeline-transform
pytest tests/unit/test_pii_macros.py

These tests verify the compiled SQL of the dbt macros using a mock dbt project.