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:

Download from BigQuery using this skill (Step 4 above)
Insert to ClickHouse via chunked_backfill.sh
Verify ClickHouse state using historical-backfill-execution skill
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