bigquery-ethereum-data-acquisition

name	bigquery-ethereum-data-acquisition
description	Workflow for acquiring historical Ethereum blockchain data using Google BigQuery free tier. Empirically validated for cost estimation, streaming downloads, and DuckDB integration. Use when planning bulk historical data acquisition or comparing data source options for blockchain network metrics.

BigQuery Ethereum Data Acquisition

Overview

Workflow for acquiring 5 years of Ethereum blockchain data (2020-2025, ~12.44M blocks) using Google BigQuery's public dataset within free tier limits. Includes column selection analysis for ML/time-series forecasting, cost optimization, and empirically validated download workflows.

Status: ✅ Empirically validated (v0.2.0, 2025-11-07)

When to Use This Skill

Use when:

• Evaluating BigQuery as a data source for Ethereum historical data
• Planning bulk historical blockchain data acquisition
• Optimizing column selection for feature engineering
• Calculating query costs and free tier utilization
• Comparing BigQuery vs RPC polling approaches
• Streaming downloads without BigQuery storage

Core Workflow

This skill follows a 5-step workflow for acquiring Ethereum data from BigQuery:

Step	Purpose	Output	Key Metric
1. Free Tier Limits	Understand query/storage limits	Limits documented	1 TB query, 10 GB storage
2. Column Selection	Optimize for ML/time-series	11 columns chosen	0.97 GB (97% savings)
3. Cost Validation	Dry-run query cost	Cost estimate	0.1% of free tier
4. Stream Download	Parquet download (no storage)	.parquet file	62 bytes/row
5. DuckDB Import	Load for analysis	DuckDB database	<100ms query time

Detailed workflow: See references/workflow-steps.md for complete step-by-step guide with SQL queries, bash commands, and validated results for each step.

Quick start: Run uv run scripts/test_bigquery_cost.py to validate cost, then uv run scripts/download_bigquery_to_parquet.py <start> <end> <output> to download.

Cost Analysis

Optimized selection: 11 columns = 0.97 GB (0.1% of free tier, 97% cost savings vs all 23 columns)

Key finding: BigQuery is 624x faster than RPC polling (<1 hour vs 26 days for 12.44M blocks).

Full analysis: See references/cost-analysis.md for detailed cost comparison, column selection rationale, and RPC provider comparison.

Prerequisites

One-time setup: gcloud auth, Python dependencies (google-cloud-bigquery, pandas, pyarrow, db-dtypes)

Setup guide: See references/setup-guide.md for complete authentication setup, dependency installation, and verification commands.

Scripts

Validated scripts (v0.2.0):

• test_bigquery_cost.py - Dry-run cost estimation (0.97 GB for 12.44M blocks)
• download_bigquery_to_parquet.py - Streaming Parquet download (62 bytes/row validated)

Templates and usage: See scripts/README.md for complete usage examples, dependencies, and validated results.

References

Workflow Documentation

• references/workflow-steps.md - Complete 5-step workflow with SQL queries, bash commands, and validated results
• references/cost-analysis.md - Detailed cost comparison, column selection rationale, RPC provider comparison
• references/setup-guide.md - Authentication setup, dependencies, verification commands

Research Documents

• references/bigquery_cost_comparison.md - Empirical cost analysis (6 vs 11 vs 23 columns)
• references/ethereum_columns_ml_evaluation.md - Column-by-column ML value analysis
• references/bigquery_complete_ethereum_data.md - Complete dataset catalog (11 tables)
• references/bigquery_cost_estimate.md - Free tier limits and methodology
• references/littleblack-hardware-report.md - Local vs cloud hardware comparison

Scripts & Validation

• scripts/README.md - Complete script usage guide with validated results
• VALIDATION_STATUS.md - Empirical test results, testing methodology, dependencies validated

Verification After Acquisition

Important: This skill covers data acquisition from BigQuery (downloading historical Ethereum blocks), but does NOT verify the data actually landed in ClickHouse.

After completing BigQuery download:

• Use the historical-backfill-execution skill
• Run scripts/clickhouse/verify_blocks.py to verify database state
• Confirm expected block count (~23.8M blocks for 2015-2025 backfill)

Common workflow:

1. Download from BigQuery using this skill (Step 4 above)
2. Insert to ClickHouse via chunked_backfill.sh
3. Verify ClickHouse state using historical-backfill-execution skill
4. Check yearly breakdown to ensure complete coverage

See historical-backfill-execution skill for database verification and troubleshooting missing data.

Related Skills

• historical-backfill-execution - ClickHouse database verification and backfill operations
• blockchain-rpc-provider-research - RPC rate limit comparison and provider evaluation
• blockchain-data-collection-validation - Empirical validation workflow for data pipelines