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bigquery-ethereum-data-acquisition

Acquire Ethereum blockchain data from BigQuery and export to Parquet.

Updated Nov 4, 2025
Search Tags:#machine learning#blockchain#cost optimization#BigQuery#Parquet#data acquisition#Ethereum
One-click install
npx skills add https://github.com/terrylica/gapless-network-data --skill bigquery-ethereum-data-acquisition
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bigquery-ethereum-data-acquisition
Source: https://github.com/terrylica/gapless-network-data/tree/main/.claude/skills/bigquery-ethereum-data-acquisition
Command: npx skills add https://github.com/terrylica/gapless-network-data --skill bigquery-ethereum-data-acquisition

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires google-cloud-bigquery, pandas, pyarrow, db-dtypes, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Provides a 624x faster and cost-optimized way to acquire historical Ethereum blockchain data from Google BigQuery, eliminating the need for slow RPC polling and ensuring data is ready for ML and time-series analysis. It prevents wasted time and budget on irrelevant data.

Core Features & Use Cases

  • Cost-Optimized Column Selection: Selects 11 essential columns for ML, reducing query costs by 97% (0.97 GB vs 34.4 GB for all columns) and staying well within BigQuery's free tier.
  • High-Speed Data Acquisition: Download 12.44 million blocks in under an hour, significantly faster than RPC polling (which can take 26+ days), accelerating your data projects.
  • Streaming to Parquet: Directly stream data to Parquet files, avoiding BigQuery storage costs and enabling efficient loading into analytical databases like DuckDB.
  • Use Case: You need 5 years of Ethereum block data for a new ML model. Use this skill to quickly download the optimized 11-column dataset from BigQuery, verify its cost (0.1% of free tier), and load it into DuckDB for immediate feature engineering.

Quick Start

Download 5 years of historical Ethereum blockchain data from BigQuery. Optimize the column selection for machine learning and ensure it stays within the free tier. Save the data to a Parquet file.

Frequently Asked Questions about bigquery-ethereum-data-acquisition

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I download Ethereum blockchain data from BigQuery without exceeding free tier costs?▼

Download Ethereum blockchain data from BigQuery by selecting 11 essential columns optimized for ML, reducing query costs to 0.97 GB (97% savings) and staying well within the free tier. This approach downloads 12.44 million blocks in under an hour with cost validation included.

Can I export BigQuery Ethereum data directly to Parquet files instead of storing in BigQuery?▼

Yes, stream Ethereum data directly to Parquet files from BigQuery, avoiding storage costs and enabling immediate loading into analytical databases like DuckDB for feature engineering and time-series analysis.

What's the fastest way to acquire 5 years of historical Ethereum blocks for machine learning?▼

Acquire 5 years of Ethereum block data 624x faster than RPC polling by querying BigQuery's public Ethereum dataset with optimized column selection, completing downloads of 12.44 million blocks in under one hour.

Does BigQuery's Ethereum public dataset work with pandas and PyArrow for ML workflows?▼

Yes, BigQuery Ethereum data integrates with pandas and PyArrow for ML workflows. This skill uses both libraries to stream cost-optimized column selections into Parquet files for immediate feature engineering and time-series forecasting.

Why would I use BigQuery instead of direct RPC polling to get Ethereum block data?▼

BigQuery Ethereum queries complete in under an hour versus 26+ days with RPC polling, cost 97% less through column optimization, and eliminate bandwidth bottlenecks. The public dataset provides validated, structured data ready for ML analysis.

Can I perform dry-run cost estimation before querying large Ethereum datasets?▼

Yes, validate query costs before execution to confirm you stay within BigQuery's free tier. This skill includes dry-run cost estimation for Ethereum block queries, showing exact GB scanned before committing resources.

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