EVM Data Export for Snowflake, AWS, Google Cloud etc
Bitquery provides blockchain data dumps for EVM-base chains like Ethereum, BSC, Base, Polygon/Matic, Optimism, etc. in parquet format that you can host directly in your own cloud (for example AWS S3) and plug into your analytics stack or data lake.
Available Topics
For EVM chains we currently provide the following topics:
- Blocks – sample file
- Balance Updates – sample file
- DEX Pools – sample file
- DEX Trades – sample file
- Uncle Blocks – sample file
- Transactions – sample file
- Transfers – sample file
Sample Ethereum Cloud Dataset
To explore the schema and test your tooling, use our public sample EVM datasets on GitHub:
The GitHub repository includes one sample file. The complete list of Parquet files is stored in our public S3 bucket and can be accessed directly. For example:
https://bitquery-blockchain-dataset.s3.us-east-1.amazonaws.com/ethereum/balance_updates/24053500_24053549.parquet
bitquery-blockchain-dataset/
└── ethereum/
├── balance_updates/
│ ├── 24053500_24053549.parquet
│ ├── 24053550_24053599.parquet
│ ├── 24053600_24053649.parquet
│ ├── 24053650_24053699.parquet
│ ├── 24053700_24053749.parquet
│ ├── 24053750_24053799.parquet
│ ├── 24053800_24053849.parquet
│ ├── 24053850_24053899.parquet
│ ├── 24053900_24053949.parquet
│ └── 24053950_24053999.parquet
├── blocks/
│ ├── 24053500_24053549.parquet
│ ├── 24053550_24053599.parquet
│ ├── 24053600_24053649.parquet
│ ├── 24053650_24053699.parquet
│ ├── 24053700_24053749.parquet
│ ├── 24053750_24053799.parquet
│ ├── 24053800_24053849.parquet
│ ├── 24053850_24053899.parquet
│ ├── 24053900_24053949.parquet
│ └── 24053950_24053999.parquet
├── calls/
│ ├── 24053500_24053549.parquet
│ ├── 24053550_24053599.parquet
│ ├── 24053600_24053649.parquet
│ ├── 24053650_24053699.parquet
│ ├── 24053700_24053749.parquet
│ ├── 24053750_24053799.parquet
│ ├── 24053800_24053849.parquet
│ ├── 24053850_24053899.parquet
│ ├── 24053900_24053949.parquet
│ └── 24053950_24053999.parquet
├── dex_trades/
│ ├── 24053500_24053549.parquet
│ ├── 24053550_24053599.parquet
│ ├── 24053600_24053649.parquet
│ ├── 24053650_24053699.parquet
│ ├── 24053700_24053749.parquet
│ ├── 24053750_24053799.parquet
│ ├── 24053800_24053849.parquet
│ ├── 24053850_24053899.parquet
│ ├── 24053900_24053949.parquet
│ └── 24053950_24053999.parquet
├── events/
│ ├── 24053500_24053549.parquet
│ ├── 24053550_24053599.parquet
│ ├── 24053600_24053649.parquet
│ ├── 24053650_24053699.parquet
│ ├── 24053700_24053749.parquet
│ ├── 24053750_24053799.parquet
│ ├── 24053800_24053849.parquet
│ ├── 24053850_24053899.parquet
│ ├── 24053900_24053949.parquet
│ └── 24053950_24053999.parquet
├── miner_rewards/
│ ├── 24053500_24053549.parquet
│ ├── 24053550_24053599.parquet
│ ├── 24053600_24053649.parquet
│ ├── 24053650_24053699.parquet
│ ├── 24053700_24053749.parquet
│ ├── 24053750_24053799.parquet
│ ├── 24053800_24053849.parquet
│ ├── 24053850_24053899.parquet
│ ├── 24053900_24053949.parquet
│ └── 24053950_24053999.parquet
├── transactions/
│ ├── 24053500_24053549.parquet
│ ├── 24053550_24053599.parquet
│ ├── 24053600_24053649.parquet
│ ├── 24053650_24053699.parquet
│ ├── 24053700_24053749.parquet
│ ├── 24053750_24053799.parquet
│ ├── 24053800_24053849.parquet
│ ├── 24053850_24053899.parquet
│ ├── 24053900_24053949.parquet
│ └── 24053950_24053999.parquet
├── transfers/
│ ├── 24053500_24053549.parquet
│ ├── 24053550_24053599.parquet
│ ├── 24053600_24053649.parquet
│ ├── 24053650_24053699.parquet
│ ├── 24053700_24053749.parquet
│ ├── 24053750_24053799.parquet
│ ├── 24053800_24053849.parquet
│ ├── 24053850_24053899.parquet
│ ├── 24053900_24053949.parquet
│ └── 24053950_24053999.parquet
└── uncle_blocks/
├── 15535500_15535549.parquet
├── 15535550_15535599.parquet
├── 15535600_15535649.parquet
├── 15535650_15535699.parquet
├── 15535700_15535749.parquet
├── 15535750_15535799.parquet
├── 15535800_15535849.parquet
└── 15535850_15535899.parquet
Use these samples to:
- Validate your ETL / analytics pipeline against realistic EVM data.
- Inspect column names and types before connecting to full buckets.
- Benchmark query performance on your preferred engines and hardware.
Other Ways to Access EVM Data
Cloud data dumps are ideal for batch analytics and historical workloads.
If you need low-latency real-time data, you can also consume Bitquery streams via Kafka and GraphQL subscriptions.
- Kafka-based real-time streams (mempool and committed data) are documented here:
Kafka Streaming Concepts