BigQuery Ethereum Data Acquisition

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:

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