Financial Services Data Pipeline

A comprehensive, production-ready data pipeline for financial services customer risk assessment and regulatory reporting, built using the most popular tools in the modern data stack.

Architecture Overview

This pipeline demonstrates industry best practices and the most widely adopted tools for each component:

• Orchestration: Apache Airflow (industry standard)
• Ingestion: Fivetran (Salesforce), Debezium CDC (Oracle), Python APIs (Credit Bureaus)
• Transformation: dbt (analytics engineering standard)
• Data Quality: Great Expectations (most popular validation framework)
• Storage: S3 Data Lake + Snowflake Data Warehouse
• ML Operations: MLflow (most popular ML lifecycle tool)
• Monitoring: Built-in Airflow monitoring + DataDog

Pipeline Components

1. Data Ingestion (Stage 1)

• Salesforce CRM: Fivetran managed connector for automatic sync
• Oracle Banking System: Debezium CDC for real-time transaction capture
• Credit Bureau APIs: Python scripts with resilience patterns
• Market Data: External financial data APIs (Alpha Vantage, FRED)

2. Data Quality & Validation (Stage 2)

• Great Expectations: Comprehensive data quality validation
• Schema Drift Detection: Automated schema change monitoring
• Data Profiling: Statistical analysis and anomaly detection

3. Data Transformation (Stage 3)

• dbt Models: SQL-based transformations with testing
• Staging Layer: Clean and standardize raw data
• Marts Layer: Business-ready analytical datasets
• Incremental Processing: Efficient handling of large datasets

4. Machine Learning & Risk Scoring (Stage 4)

• MLflow: Model lifecycle management and experiment tracking
• Risk Assessment: Customer risk scoring using multiple data sources
• Model Validation: A/B testing and performance monitoring
• Batch Inference: Daily risk score generation

5. Regulatory Reporting (Stage 5)

• Compliance Reports: Automated generation of regulatory reports
• Data Lineage: Complete audit trail for regulatory requirements
• Secure Export: Encrypted transmission to regulatory authorities

6. Monitoring & Operations (Stage 6)

• SLA Monitoring: Data freshness and processing time tracking
• Business Metrics: Key performance indicators and dashboards
• Data Catalog: Automated documentation and discovery

Project Structure

sample-pipeline/
├── airflow/
│   ├── dags/
│   │   └── financial_services_pipeline.py    # Main DAG
│   ├── plugins/                              # Custom operators
│   └── config/
│       └── great_expectations.yml            # Data quality config
├── dbt/
│   ├── models/
│   │   ├── staging/                          # Staging models
│   │   │   ├── stg_salesforce_accounts.sql
│   │   │   └── stg_oracle_transactions.sql
│   │   ├── marts/                            # Business marts
│   │   │   └── customer_risk_assessment.sql
│   │   └── schema.yml                        # Tests & documentation
│   └── dbt_project.yml                       # dbt configuration
├── scripts/
│   └── extract_market_data.py                # Market data extraction
├── requirements.txt                          # Python dependencies
└── README.md

Key Features

Modern Data Stack Integration

• Uses the most popular tool for each pipeline component
• Cloud-native architecture with managed services where possible
• Infrastructure-as-code approach for reproducibility

Financial Services Specific

• Regulatory compliance built-in (Basel III, CCAR, AML)
• Risk assessment and fraud detection patterns
• Secure handling of PII and financial data
• Audit trails and data lineage for compliance

Production-Ready Patterns

• Comprehensive error handling and retries
• Data quality validation at every stage
• Monitoring and alerting integration
• Security and access control best practices

Scalability & Performance

• Incremental data processing
• Parallel execution where possible
• Cloud warehouse optimizations
• Cost-effective storage tiering

Data Quality & Testing

The pipeline includes comprehensive data quality checks:

• Source Data Validation: Great Expectations suites for each data source
• Transformation Testing: dbt tests for data quality and business logic
• End-to-End Validation: Pipeline-level checks for completeness and accuracy
• Regulatory Compliance: Specific tests for regulatory reporting requirements

Security & Compliance

• Encryption: Data encrypted at rest and in transit
• Access Control: Role-based permissions for all components
• Audit Logging: Complete audit trail for all data access and modifications
• PII Protection: Column-level encryption for sensitive data
• Regulatory Reporting: Automated compliance with financial regulations

Getting Started

Prerequisites

• Apache Airflow 2.8+
• dbt 1.7+
• Python 3.9+
• Access to cloud data warehouse (Snowflake/BigQuery)
• AWS S3 for data lake storage

Installation

1. Install dependencies:

   pip install -r requirements.txt

2. Configure connections in Airflow:

   • snowflake_default: Data warehouse connection
   • aws_default: S3 data lake access
   • API keys for external data sources

3. Initialize dbt project:

   cd dbt && dbt deps && dbt run

4. Deploy Great Expectations:

   great_expectations init

Running the Pipeline

The pipeline runs daily at 2 AM UTC with the following execution flow:

1. Data Ingestion (parallel): Fivetran sync, CDC processing, API extracts
2. Data Quality Validation: Great Expectations validation suites
3. Data Transformation: dbt staging → marts → warehouse loading
4. ML & Risk Scoring: Model training and batch inference
5. Regulatory Reporting: Compliance report generation and export
6. Monitoring: SLA checks and business metrics

Popular Tools Used

This pipeline showcases the most adopted tools in each category:

Category	Tool	Why It's Popular
Orchestration	Apache Airflow	De facto standard, rich ecosystem
ELT/Ingestion	Fivetran	Managed connectors, schema evolution
CDC	Debezium	Open source, Kafka integration
Transformation	dbt	SQL-based, version control, testing
Data Quality	Great Expectations	Comprehensive, Python ecosystem
Data Warehouse	Snowflake	Performance, ease of use
ML Ops	MLflow	Model lifecycle, experiment tracking
Data Lake	S3 + Parquet	Cost effective, widely supported

Best Practices Demonstrated

• Incremental Processing: Efficient handling of large datasets
• Data Quality First: Validation at every pipeline stage
• Schema Evolution: Handling changes in source systems
• Error Handling: Comprehensive retry and fallback logic
• Monitoring: Proactive alerting and SLA tracking
• Security: Encryption, access controls, audit logging
• Documentation: Automated data catalog and lineage
• Testing: Unit tests, integration tests, data quality tests

This pipeline represents a realistic, production-ready implementation using the most popular tools and patterns in modern data engineering for financial services.