🧾 Blockchain ETL Pipeline

March 2025 · PostgreSQL · Bitquery API · Python

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🔍 Project Overview

This project automates the extraction, transformation, and loading (ETL) of blockchain transaction data using the Bitquery API. It focuses on crypto trade data and stores cleaned results into a PostgreSQL database for further analysis. The goal was to build a reusable data pipeline for historical and real-time blockchain analytics.

⚙️ Methodology

• Extraction: Queried the Bitquery GraphQL API using Python to fetch token trade data for selected time windows.
• Transformation: Normalised JSON responses using pandas, parsed timestamps, selected relevant fields (e.g. token name, amount, value, sender/receiver), and handled missing/nulls.
• Loading: Connected to a local PostgreSQL instance using SQLAlchemy and inserted transformed data into custom schema tables.
• Automation: Designed for both batch loading (historical backfill) and near real-time streaming for future cron jobs.

💡 Features

• Supports flexible API date range queries and token filters
• Modular ETL functions for easy reuse and testing
• Connection pooling for efficient PostgreSQL insertions
• Simple CLI interface for custom token loading

📉 Errors & Fixes

• Rate-limiting on Bitquery API – implemented exponential backoff and retry logic.
• PostgreSQL schema mismatches – enforced data type casting and schema validation pre-load.
• Timestamp inconsistency – used UTC conversions and ISO format validation for ingestion consistency.

📈 Key Takeaways

• APIs with rate limits require robust retry strategies for production pipelines.
• ETL structure benefits greatly from modularisation and logging at each stage.
• This pipeline can be expanded to support multiple blockchains and used for crypto dashboards or alerts.

🔗 Repository

GitHub Repo: Blockchain ETL Pipeline

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