gcp-pipeline-ref-transform 1.0.7

GCP Pipeline Reference: Generic FDP Transformation — dbt models for ODP to FDP (JOIN + MAP patterns)

Generic Transformation

Unit 2 of Generic 3-Unit Deployment

FDP Transformation - dbt models for ODP → FDP transformation.

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Flow Diagram

                         Generic TRANSFORMATION FLOW
                         ──────────────────────

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

  odp_generic.customers ─────┐
                        │    ┌─────────────────┐
  odp_generic.accounts  ─────┼───►│   JOIN Logic    │────────► fdp_generic.event_transaction_excess
                        │    └─────────────────┘
                        │
  odp_generic.decision  ─────┼───────────────────────────────► fdp_generic.portfolio_account_excess
                        │
                        └───────────────────────

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Pattern

MULTI-TARGET:

1. JOIN: 2 ODP sources (customers, accounts) → 1 FDP target (event_transaction_excess)
2. MAP: 1 ODP source (decision) → 1 FDP target (portfolio_account_excess)

Step	Description
1	Staging models clean and type-cast raw ODP data
2	add_audit_columns macro injects run_id and source_file
3	mask_pii macro applies environment-aware masking to sensitive fields
4	event_transaction_excess performs INNER JOIN between Customers and Accounts
5	portfolio_account_excess maps Decision ODP 1:1 to FDP

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Data Mapping

Source Table	Key Fields
odp_generic.customers	customer_id, ssn, first_name, last_name, dob, status
odp_generic.accounts	account_id, customer_id, account_type, balance, open_date
odp_generic.decision	decision_id, customer_id, application_id, decision_code, score, decision_date

Target Table	Description
fdp_generic.event_transaction_excess	Joined customer-account view
fdp_generic.portfolio_account_excess	Decision-based portfolio view

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Components

Directory	Purpose
dbt/models/staging/generic/	Staging models (clean raw data)
dbt/models/fdp/	FDP models (JOIN and MAP logic)

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Library-Driven Ease of Use

The Generic transformation unit uses the gcp-pipeline-transform library to ensure data privacy and lineage with zero local macro development:

1. Zero-Bleed PII Masking: Uses {{ mask_pii(column, 'SSN') }}. The library automatically applies the correct mask (Full in Prod, Partial in Staging) based on the environment.
2. Automated Lineage: Uses {{ add_audit_columns() }} to inject run_id and source_file variables, maintaining the E2E lineage established in the ingestion layer.
3. Metadata Enrichment: Replaces hardcoded business logic with generic library macros that interpret rules from the EntitySchema.

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How to Replicate this JOIN Transformation (3-to-1)

To create a new transformation unit that joins multiple entities, follow the Creating New Deployment Guide.

Key steps for this JOIN pattern:

1. Register Library: Point your dbt_project.yml to the gcp-pipeline-transform macro paths.
2. Staging Models: Create views for your ODP tables. Use add_audit_columns for consistency.
3. FDP Models: Implement your LEFT JOIN logic. Apply mask_pii to all sensitive fields.
4. Governance: Run validate_no_pii_in_export in your CI/CD to prevent leakage.

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Infrastructure & Configurations

Google Cloud Resources

This deployment requires the following GCP infrastructure, provisioned via Terraform:

• Data Warehouse: BigQuery datasets odp_generic (source) and fdp_generic (target).
• Processing: dbt (running on Cloud Composer or as a standalone process) for executing transformations.

For detailed infrastructure definitions, see infrastructure/terraform/systems/generic/transformation/.

dbt Configuration (dbt_project.yml)

The transformation behavior is controlled by variables and configurations in dbt_project.yml:

Variable	Description	Default / Source
gcp_project_id	Target GCP Project	GCP_PROJECT_ID env var
source_dataset	Source ODP dataset	odp_generic
staging_dataset	Intermediate staging dataset	stg_generic
fdp_dataset	Target FDP dataset	fdp_generic
marts_dataset	Marts dataset	marts_generic
analytics_dataset	Analytics dataset	analytics_generic
extract_date	Date of data extract	null (optional filter)
generic_entities	List of entities to process	['customers', 'accounts', 'decision']
masking_level	PII masking strategy (FULL, PARTIAL, NONE)	AUTO

Technology Stack & Documentation

• Google BigQuery - Serverless data warehouse
• dbt (data build tool) - Transformation workflow
• dbt-bigquery Adapter - dbt to BigQuery connector
• Data Modeling in dbt - Best practices for models

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Dependencies

Library	Purpose
dbt-bigquery	dbt adapter for BigQuery
gcp-pipeline-transform	Shared macros (audit columns)

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Execution & Testing

1. Local Development Setup

Initialize the virtual environment:

./scripts/setup_deployment_venv.sh bigquery-to-mapped-product
source deployments/bigquery-to-mapped-product/venv/bin/activate

2. Local dbt Execution

Run dbt models locally against the development BigQuery dataset:

cd dbt
dbt run --profiles-dir . --target dev

3. Data Quality Validation

Run dbt tests to verify transformation logic and PII masking:

dbt test --profiles-dir . --target dev

4. Governance Verification

Use the library macro to ensure no unmasked PII exists in your models before deployment:

{{ validate_no_pii_in_export('fdp_generic.event_transaction_excess') }}

5. Cloud Execution

In production, this unit is triggered by the generic_odp_load_dag once ingestion is successful. The transformation is executed via a BashOperator running dbt run.

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SQL Example

-- fdp_generic.event_transaction_excess
SELECT
    c.customer_id,
    c.first_name,
    c.last_name,
    a.account_id,
    a.current_balance,
    -- Audit columns
    c._run_id,
    CURRENT_TIMESTAMP() as _transformed_at
FROM {{ ref('stg_generic_customers') }} c
JOIN {{ ref('stg_generic_accounts') }} a 
    ON c.customer_id = a.customer_id