FinFlow Analytics 📊

Enterprise Financial Data Analytics Platform with dbt & BigQuery 💰

Table of Contents 📑

• Overview 🎯
• Project Structure 📁
• Data Architecture 🏗️
• Installation & Setup 🚀
• Development Workflow 💻
• Contributing 🤝

Overview 🎯

FinFlow Analytics is a comprehensive dbt (data build tool) project designed for financial data analytics using BigQuery as the data warehouse. The project implements a modern data modeling approach with bronze (raw), silver (intermediate), and gold (business-ready) layers, featuring robust data quality testing and financial metric calculations.

Project Structure 📁

finflow_analytics/
├── analyses/           # Ad-hoc analytical queries
├── macros/            # Reusable SQL snippets and utilities
├── models/            # Core data transformation logic
│   ├── bronze/        # Raw data ingestion layer
│   ├── silver/        # Intermediate transformations
│   └── gold/          # Business-ready data models
├── tests/             # Data quality tests
├── snapshots/         # Change data capture configurations
├── dbt_project.yml    # Project configurations
├── packages.yml       # External package dependencies
└── sources.yml        # Source definitions and tests

Data Architecture 🏗️

Layer Overview 🔄

1. Bronze Layer (Raw) 🥉

• Direct source data ingestion with minimal transformation
• Incremental loading with merge strategy
• Source freshness monitoring

2. Silver Layer (Intermediate) 🥈

• Account metrics and risk analysis
• Customer behavior analytics
• Transaction pattern analysis
• Product adoption metrics

3. Gold Layer (Business) 🥇

• Dimension tables (customer, account, product, location, date)
• Fact tables (transactions, account balances)
• Business-ready metrics

Model Dependencies 🔗

graph TD
    A[src_transactions] --> B[int_transaction_metrics]
    C[src_accounts] --> D[int_account_metrics]
    C --> E[int_account_risk_metrics]
    F[src_customers] --> G[int_customer_metrics]
    F --> H[int_customer_product_metrics]
    I[src_products] --> H

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Installation & Setup 🚀

1. Environment Setup ⚙️

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

# Install dependencies
pip install dbt-bigquery
pip install dbt-utils

2. BigQuery Configuration 🔧

# ~/.dbt/profiles.yml
finflow_analytics:
  target: dev
  outputs:
    dev:
      type: bigquery
      method: oauth/service-account
      project: finflow-analytics-production
      dataset: finflow_data
      location: US-CENTRAL1
      threads: 4

3. Project Initialization 🏁

git clone https://github.com/ifyjakande/finflow-analytics.git
cd finflow-analytics
dbt deps
dbt seed
dbt run --full-refresh

Development Workflow 💻

Daily Operations ⚡

# Check source freshness
dbt source freshness

# Run incremental updates
dbt run --models tag:daily

# Run tests
dbt test

# Generate docs
dbt docs generate
dbt docs serve

Model Configuration ⚙️

models:
  finflow_analytics:
    bronze:
      +materialized: incremental
      +incremental_strategy: merge
      +schema: bronze
    silver:
      +materialized: view
      +schema: silver
    gold:
      +materialized: table
      +schema: gold

Contributing 🤝

Development Process 🛠️

1. Fork the repository
2. Create feature branch: git checkout -b feature/your-feature-name
3. Make changes and test: dbt test && dbt run
4. Submit PR with clear description and tests

Code Style Guidelines 📝

• Use uppercase for SQL keywords
• Include model documentation
• Follow naming conventions for models and tests
• Add appropriate tests for new features

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Last updated: January 21, 2025 �