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Financial Data Pipeline

ETL Automation • Market Data • Research Infrastructure
Production-Scale
Records Processing
High-Availability
System Reliability
Multiple
Data Sources

Objective

Build a modular, production-ready ETL pipeline for financial market data that supports extraction, transformation, validation, and storage from multiple sources (crypto, equities, derivatives) with analytics and database integration.

Methodology

The pipeline implements a complete ETL workflow with modular architecture for scalability and maintainability.

Data Extraction

Multiple source integration (Bybit, Binance, Yahoo Finance)

Data Validation

Comprehensive OHLCV validation & quality checks

Data Processing

Automated cleaning, outlier detection, missing data handling

Storage Layer

TimescaleDB/PostgreSQL with time-series optimization

Export Formats

Parquet, CSV, JSON output options

Monitoring

Data quality metrics & performance tracking

Results

Production-scale processing with high-availability architecture

Automated quality checks with comprehensive validation

Modular architecture supporting multiple data sources

TimescaleDB optimization for time-series queries

Challenges

  • Maintaining estimator robustness during market regime shifts
  • Ensuring fault tolerance for continuous 24/7 operation
  • Handling multiple data sources with different formats and structures
  • Implementing comprehensive data quality validation
  • Optimizing performance for large-scale data processing

Technologies Used

PythonPandasPostgreSQLTimescaleDBGitDocker

Key Features

Data Quality

  • • Comprehensive validation & quality scoring
  • • Automated outlier detection (IQR method)
  • • Missing value imputation strategies
  • • OHLCV relationship validation

Storage & Processing

  • • TimescaleDB for time-series optimization
  • • Chunked processing for large datasets
  • • Multiple export formats (Parquet, CSV, JSON)
  • • Data lineage tracking

Integration

  • • Multiple data providers (Bybit, Yahoo Finance)
  • • REST API integration
  • • Rate limiting & error handling
  • • Extensible provider architecture

Monitoring

  • • Real-time quality metrics
  • • Performance monitoring
  • • Comprehensive logging system
  • • Data quality dashboards
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