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.

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. 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
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
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
- 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

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
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
```

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

Key Technologies Demonstrated

Apache Airflow - Workflow orchestration and task scheduling
Python Data Processing - API integration and data transformation
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!