Finance Data Pipeline Project

📈 Finance Data Pipeline Project

Quick Preview: A data pipeline designed to automate the extraction, processing, and storage of financial data using Apache Airflow, Twelve Data API, and Google Cloud Storage.

🎯 Project Overview

This project provides a simple yet powerful data pipeline that automates the process of fetching financial data from an API, converting it into a CSV file, and uploading it to Google Cloud Storage. The process is fully automated using Apache Airflow, which schedules and manages the various tasks involved in the pipeline.

Finance Data Pipeline

⚡ Key Features

📊 Automated Data Extraction: Fetches financial data from the Twelve Data API
🔄 Data Processing: Converts the API response into a CSV file for easy storage and analysis
☁️ Cloud Storage: Uploads the CSV file to a Google Cloud Storage bucket
🛠️ Orchestration with Apache Airflow: Manages and schedules tasks in the pipeline, ensuring reliable and repeatable processes
📈 Real-time Financial Data: Access to real-time and historical stock information

🔍 Click to see detailed technical implementation

🛠️ Technical Architecture

Core Technologies

Python - Core programming language used for data extraction and processing
Apache Airflow - Orchestrates the pipeline, handling task dependencies and scheduling
Google Cloud Platform (GCP) - Used for storing the processed data in a GCS bucket
Twelve Data API - Source of financial data, providing real-time and historical stock information
CSV - Format used to store the extracted data

Pipeline Architecture

# Simplified DAG structure for finance data pipeline
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'finance-team',
    'depends_on_past': False,
    'start_date': datetime(2024, 1, 1),
    'email_on_failure': True,
    'retries': 2,
    'retry_delay': timedelta(minutes=5)
}

dag = DAG(
    'finance_data_pipeline',
    default_args=default_args,
    description='Finance data processing pipeline',
    schedule_interval='@daily',
    catchup=False
)

Data Processing Workflow

Data Extraction
- Connect to Twelve Data API
- Fetch real-time and historical stock data
- Handle API rate limits and authentication
Data Processing
- Parse JSON response from API
- Clean and validate financial data
- Convert data into structured CSV format
Data Storage
- Upload processed CSV files to Google Cloud Storage
- Organize files with proper naming conventions
- Implement data retention policies
Monitoring & Logging
- Track pipeline execution status
- Log errors and performance metrics
- Send notifications on pipeline failures

🔧 Key Components

Data Sources

Twelve Data API: Comprehensive financial data provider
Real-time Market Data: Live stock prices and market information
Historical Data: Historical stock performance and trends

Processing Features

Error Handling: Robust error handling and retry mechanisms
Data Validation: Ensure data quality and completeness
Scalable Architecture: Handle varying data volumes efficiently

Storage & Output

Google Cloud Storage: Secure and scalable cloud storage
CSV Format: Industry-standard format for data analysis
Organized Structure: Logical file organization for easy access

🚀 Installation & Usage

Prerequisites

Python 3.x installed on your system
Apache Airflow installed and configured
Google Cloud SDK installed and authenticated with access to your GCS bucket
API Key from Twelve Data API

Setup Steps

Clone the Repository:

git clone https://github.com/ahmedmakroum/pipelinestocks.git
cd finance-data-pipeline

Install Python Dependencies:
```
pip install -r requirements.txt
```
Set Up Airflow:
- Place the dag.py script in your Airflow DAGs directory
- Start the Airflow scheduler and web server:
```
airflow scheduler
airflow webserver
```
Configure Google Cloud Storage:
- Ensure your GCP credentials are set up and your project is selected
- Update the bucket name in dag.py to match your GCS bucket

Running the Pipeline

Triggering the DAG:
- Access the Airflow web UI at http://localhost:8080
- Locate the finance_data_pipeline DAG
- Trigger the DAG manually or set it to run on a schedule
Monitoring:
- Monitor the DAG’s execution from the Airflow UI
- Check the GCS bucket to verify that the CSV files are being uploaded correctly

🔗 Project Links

📂 GitHub Repository - Complete source code and documentation
📊 Twelve Data API - Financial data API documentation
☁️ Google Cloud Storage - Cloud storage documentation

🎯 Key Technologies Demonstrated

✅ Apache Airflow - Workflow orchestration and task scheduling
✅ Python Data Processing - API integration and data transformation
✅ Google Cloud Platform - Cloud storage and data management
✅ Financial Data APIs - Real-time market data integration
✅ CSV Processing - Data format conversion and storage

🔮 Future Enhancements

Real-time Streaming: Implement real-time data streaming for live market updates
Data Analytics: Add analytical capabilities for financial insights
Multi-source Integration: Support for additional financial data providers
Machine Learning: Integrate ML models for predictive analytics
Dashboard Integration: Create interactive dashboards for data visualization

🤝 Contributing

Contributions are welcome! Please fork the repository and submit a pull request for review.

Interested in financial data engineering or building automated trading systems? Let’s connect and discuss how modern data pipelines can enhance financial analysis and decision-making!