Blockchain Data in Cloud

Bitquery sells full-history blockchain datasets as Parquet files through the Bitquery Data Store. The pages in this section document these Data Store exports. Pick a chain or protocol dataset, check the schema and a free sample, pay a fixed price by card or bank transfer, and download from S3 or Cloudflare R2. The same files load into Snowflake, BigQuery, Google Cloud Storage, DuckDB, Spark or any other Parquet reader. This page documents what each dataset contains and how the files are laid out. Buying happens on the Data Store.

Looking to buy a dataset?

Browse all datasets on the Data Store. Each listing shows the tables and columns, a free sample to test before you pay, and the price. The listing is not the full catalogue. For a chain, table or date range that is not listed, email sales@bitquery.io.

Bitquery provides ready-to-use blockchain Data Store exports in Parquet format via popular cloud providers such as AWS S3, Google Cloud Storage, Snowflake, and BigQuery.
You can plug these datasets directly into your existing analytics stack and build custom data pipelines without running your own blockchain infrastructure or maintaining complex indexing systems.

Overview

Data Store exports deliver production-ready blockchain datasets optimized for large-scale analytics, historical backfills, and data lake integrations. All data is provided in Apache Parquet format, ensuring optimal compression, columnar storage, and compatibility with modern analytics engines. We can also provide other file formats if required.

Key Benefits

No Infrastructure Management – Skip running blockchain nodes, indexers, or data processing infrastructure
Production-Ready Format – Parquet files optimized for analytics workloads
Cloud-Native – Direct integration with AWS S3, Google Cloud Storage, Snowflake, and BigQuery
Historical Coverage – Full history from genesis for most chains; the Hyperliquid Data Store listing is the last 30 days, and older ranges are available on request
Multi-Chain Support – Access data from major blockchain networks
Cost-Effective – Pay only for the data you need, when you need it
Scalable – Built for large historical backfills

Available Data Store Exports

Bitquery provides Data Store exports for the following blockchains:

EVM Chains Data Export

Export blockchain data for Ethereum, BSC, Base, Polygon/Matic, Optimism, Arbitrum, Robinhood, and other EVM-compatible chains. Includes:

Blocks – Block-level metadata and timestamps
Transactions – Full transaction-level data with gas information
Transfers – Native token and ERC-20 token transfers
Balance Updates – Account balance changes per block
Balances – Daily end-of-day balance snapshots per account and token
DEX Trades – Decentralized exchange trading data
DEX Pools – Liquidity pool metadata and state
Smart Contract Calls – Function calls and interactions
Events – Ethereum event logs and emissions
Miner Rewards – Block rewards and transaction fees
Uncle Blocks – Ethereum uncle block data

Use Cases: DeFi analytics, NFT tracking, smart contract analysis, token holder analysis, DEX volume analysis, cross-chain analytics.

Buy on the Data Store: Ethereum Transfers · BSC Transfers · Base Transfers · Polygon Transfers · Arbitrum Transfers

Solana Blockchain Data Export

Export Solana blockchain data including slot-level blocks, transactions, transfers, and DEX activity:

Blocks – Slot-level block metadata
Transactions – Full transaction-level data with signatures
Transfers – Native SOL and SPL token transfers
Balance Updates – Account balance changes per slot
DEX Pools – Decentralized exchange pool metadata
DEX Orders – Order-level DEX activity and fills
DEX Trades – Executed trades on Solana DEXs
Rewards – Validator and staking rewards

Use Cases: Solana DeFi analytics, NFT marketplace analysis, token transfer tracking, DEX volume analysis, validator performance monitoring.

Buy on the Data Store: Solana datasets on the Data Store, including Pump.fun, PumpSwap, Raydium AMM v4, Jupiter, Meteora DLMM and Orca Whirlpool

Tron Blockchain Data Export

Export Tron blockchain data for comprehensive network analysis:

Blocks – Block-level metadata
Transactions – Full transaction-level data
Transfers – Native TRX and TRC-20 token transfers
Balance Updates – Account balance changes per block
DEX Trades – Executed trades on Tron DEXs

Use Cases: Tron DeFi analytics, TRC-20 token tracking, DEX volume analysis, account balance monitoring, transaction flow analysis.

Buy on the Data Store: TRON Transfers

Bitcoin Blockchain Data Export

Export Bitcoin blockchain data including transaction inputs, outputs, and OMNI Layer protocol data:

Blocks – Block-level metadata
Transactions – Full transaction-level data
Inputs – Transaction input data and UTXO references
Outputs – Transaction output data and addresses
OMNI Transactions – OMNI Layer protocol transactions
OMNI Transfers – OMNI Layer token transfers

Use Cases: Bitcoin transaction analysis, UTXO tracking, address clustering, OMNI token analysis, blockchain forensics, historical price analysis.

Buy on the Data Store: Bitcoin

Ripple (XRP Ledger) Data Export

Export Ripple / XRP Ledger data, modelled around XRPL's ledger objects:

Transactions – Transaction envelope with fee, sequence, result code, memos, and signers
Transfers – Unified value movement: XRP and issued-token payments, DEX and AMM trade sides, NFT trades and mints, and fee burns
Payments – Payment transactions with requested, delivered, send-max, and deliver-min amounts
Balances – Account balance before and after each change, native and issued
Account Roots – Account state: XRP balance, owner count, sequence, domain, transfer rate
Ripple States – Trust line balances between two accounts for an issued currency
Offers – DEX order book offers, before and after each change
NFToken Offers – NFT buy and sell offers with the asking price
Escrows – Escrow creation, finish, and cancel with conditions and time locks
Checks – Deferred payment authorizations

Use Cases: XRP payment flow analysis, issued-token and stablecoin tracking, XRPL DEX and AMM volume analysis, order book reconstruction, trust line and issuer exposure analysis, NFT marketplace activity, network fee revenue analysis.

Hyperliquid (HyperCore) Data Export

Export Hyperliquid Core (HyperCore) order-book exchange data — an event stream rather than a state chain:

Trades – Both sides of every match, with realized PnL, fees, taker flag, position after the fill, execution order in the block and TWAP link
Perp Liquidations – Both sides of every liquidation, with the liquidated account, mark price and method
TWAPs – TWAP order state, executed size and notional, terminal status
Order Updates – Full order lifecycle: open, canceled, filled, triggered, and every rejection reason
Book Updates – L4 per-order book changes with the owning address and order id
Price Updates – Mark, oracle and external perp prices per HIP-3 market, and the inputs behind them
Perp Fundings – Every account's funding payment at each hourly settlement

Use Cases: Perp market microstructure research, order book reconstruction at L4, maker and taker behaviour analysis, per-wallet realized PnL and funding, liquidation forensics, HIP-3 builder market analysis, backtesting against full order flow.

Buy on the Data Store: Hyperliquid · Hyperliquid Trades · Hyperliquid Order Book · Hyperliquid Funding

Robinhood Chain Data Export

Export Robinhood Chain data — an EVM network (chain ID 4663) where tokenized equities trade alongside crypto-native assets:

DEX Trades – Buy and sell side with token metadata, USD amounts, pool and protocol
Transfers – Native and ERC-20 movements with USD value and currency metadata
Balances – Balance snapshot per address and currency, with change times and update count
Events – Decoded logs with arguments, topics, and the emitting call context
Calls – Internal and top-level calls with decoded arguments, returns, and state changes

Use Cases: Tokenized equity and RWA flow analysis, USDG stablecoin tracking, Robinhood DEX volume analysis across Uniswap and Pons, wallet balance reconstruction, contract call tracing, cross-venue arbitrage research.

Buy on the Data Store: Pons

Pump.fun Data Export

Export Pump.fun data on Solana:

Creation & Migration Events – Pump.fun token creation and migration events
DEX Trades – Executed trades on Pump.fun
DEX Pools – Liquidity pool metadata and activity
OHLCV – Open/High/Low/Close/Volume candles per token

Buy on the Data Store: Pump.fun · PumpSwap

Stellar Data Export

Export Stellar data, modelled around ledgers, transactions, operations and effects:

Blocks – Ledger-level metadata
Transactions – Transaction-level data with fee account, memo and sequence
Operations – Operation-level records with source account and operation details
Payments – Payment and path-payment operations
Transfers – Native XLM and issued-asset transfers
Effects – Ledger effects, including balance, trade, claimable balance and liquidity pool effects

Polymarket Data Export

Export Polymarket data on Polygon, joined with the market question, outcome label and collateral token. History starts in September 2025:

Prediction Trades – Outcome-token trades with market question, outcome label, price, and collateral amounts
Prediction Settlements – Position splits, merges and payout redemptions
DEX Trades – Polymarket trades in the standard EVM DEX trades schema

Use Cases: Backtesting, research, leaderboards, model training, warehouse joins.

Buy on the Data Store: Polymarket

BSC (BNB Chain) Data Export

Export BSC (BNB Chain) blockchain data for comprehensive EVM-compatible chain analysis:

Blocks – Block-level metadata and timestamps
Transactions – Full transaction-level data with gas information
Transfers – Native BNB and BEP-20 token transfers
Balance Updates – Account balance changes per block
DEX Trades – Executed trades on BSC DEXs (PancakeSwap, etc.)
DEX Pools – Liquidity pool metadata and state
Smart Contract Calls – Function calls and contract interactions
Events – BSC event logs and emissions
Miner Rewards – Block rewards and transaction fees

Use Cases: BSC DeFi analytics, PancakeSwap analysis, BEP-20 token tracking, DEX volume analysis, smart contract monitoring, yield farming analytics, NFT marketplace data.

Buy on the Data Store: BSC Transfers

Data Format and Structure

Blockchain data is provided by default in Apache Parquet format, a columnar storage file format optimized for analytics workloads. We can also provide data in other file formats (CSV, JSON, Avro, etc.) based on your requirements. Parquet offers:

High Compression – Columnar encoding and compression keep files small
Columnar Storage – Enables efficient column pruning and predicate pushdown
Schema Evolution – Supports schema changes over time
Universal Compatibility – Works with all major analytics engines

File Organization

Data is organized by blockchain and topic, with files named using block/slot ranges:

bitquery-blockchain-dataset/
├── ethereum/
│   ├── blocks/
│   ├── transactions/
│   ├── transfers/
│   ├── balance_updates/
│   ├── dex_trades/
│   └── ...
├── solana/
│   ├── blocks/
│   ├── transactions/
│   ├── transfers/
│   ├── dex_trades/
│   └── ...
├── bitcoin/
│   ├── blocks/
│   ├── transactions/
│   ├── inputs/
│   ├── outputs/
│   └── ...
├── tron/
│   ├── blocks/
│   ├── transactions/
│   ├── transfers/
│   └── ...
├── ripple/
│   ├── transactions_tx/
│   ├── transfers_tx/
│   ├── payments_tx/
│   ├── balances/
│   └── ...
├── hyperliquid/
│   ├── trades/
│   ├── order_updates/
│   ├── book_updates/
│   ├── perp_fundings/
│   └── ...
└── robinhood/
    ├── dex_trades/
    ├── transfers/
    ├── balances/
    ├── events/
    └── ...

Sample Parquet Data

To quickly explore the structure of the data and test your tooling, you can use our public sample datasets:

GitHub repository with sample Parquet files and schemas:
https://github.com/bitquery/blockchain-cloud-data-dump-sample/tree/main

In the GitHub repo, each sample file (per data point or topic) includes the exact S3 URL in a comment, so you can:

Point test pipelines to the same path
Buy the full dataset on the Data Store, or ask sales@bitquery.io for a custom export
Validate schemas before production integration

Example: Ethereum Balance Updates

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

Use Sample Data To:

Validate ETL Pipelines – Test your data processing workflows against realistic blockchain data
Inspect Schemas – Review column names, types, and data structures before production
Benchmark Performance – Measure query performance on realistic data sizes
Develop Analytics – Build and test analytics queries before full dataset access
Validate Tooling – Ensure compatibility with your analytics stack

Cloud Platform Integration

AWS S3 Integration

Store blockchain data in Amazon S3 and query with:

Amazon Athena – Serverless SQL queries on S3 data
Amazon Redshift – Data warehouse with S3 integration
AWS Glue – ETL jobs and data catalog
Amazon EMR – Spark-based analytics on S3

Google Cloud Platform Integration

Store blockchain data in Google Cloud Storage and analyze with:

BigQuery – Serverless data warehouse with native Parquet support
Dataproc – Managed Spark and Hadoop clusters
Dataflow – Stream and batch data processing
BigQuery ML – Machine learning on blockchain data

Snowflake Integration

Load blockchain data into Snowflake for:

Data Warehousing – Centralized blockchain data storage
SQL Analytics – Complex queries across multiple chains
Data Sharing – Share blockchain datasets across teams
Snowpark – Python, Java, and Scala analytics

Other Platforms

Our Parquet datasets are compatible with:

Databricks – Unified analytics platform
Apache Spark – Distributed data processing
Presto/Trino – Distributed SQL query engine
Apache Drill – Schema-free SQL queries
DuckDB – In-process analytical database

Building Scalable Real-Time Solutions

Bitquery enables you to build enterprise-grade, scalable, real-time, low-latency solutions in the cloud. Our cloud-native architecture supports both batch and streaming data pipelines for blockchain analytics.

Real-Time Streaming Architecture

Build low-latency applications using:

Kafka Streams – High-throughput, low-latency blockchain data streams
- Mempool Data – Access pending transactions before block confirmation
- Committed Data – Real-time confirmed transaction streams
- Multi-Chain Support – Stream data from Ethereum, Solana, Bitcoin, Tron, and more
- Protobuf Format – Efficient binary serialization for optimal performance
GraphQL Subscriptions – WebSocket-based real-time data subscriptions
- Live Queries – Subscribe to specific blockchain events and transactions
- Custom Filters – Filter data streams by address, token, contract, or event
- Low Latency – Live delivery for time-sensitive applications
gRPC (CoreCast) – Low-latency gRPC streams, Solana only

For raw blocks to decode and parse your own way, see the Blockchain Data Lake.

Use Cases

DeFi Analytics

DEX Volume Analysis – Track trading volumes across decentralized exchanges
Liquidity Pool Analytics – Monitor pool sizes, fees, and impermanent loss
Yield Farming Analysis – Analyze yield opportunities and risks
Token Flow Tracking – Monitor token movements between addresses

NFT Analytics

Collection Analysis – Track NFT sales, floor prices, and market trends
Marketplace Analytics – Compare performance across NFT marketplaces
Holder Analysis – Identify whale wallets and distribution patterns
Rarity Analysis – Calculate and track NFT rarity metrics

Blockchain Forensics

Transaction Tracing – Follow funds through complex transaction paths
Address Clustering – Identify related addresses and entities
Compliance Monitoring – Track suspicious transactions and patterns
Risk Assessment – Evaluate transaction risks and anomalies

Data Science and Machine Learning

Price Prediction – Build models using historical transaction data
Anomaly Detection – Identify unusual patterns in blockchain activity
Network Analysis – Analyze blockchain network topology
Sentiment Analysis – Correlate on-chain activity with market sentiment

Business Intelligence

Portfolio Tracking – Monitor multi-chain portfolio performance
Revenue Analytics – Track protocol revenues and fees
User Analytics – Analyze user behavior and engagement
Market Research – Study market trends and competitive analysis

Real-Time vs Batch Data Access

Data Store exports are optimized for batch analytics and historical workloads. They provide:

Deep Historical Data – Full history for most datasets (Hyperliquid: the Data Store lists the last 30 days, older ranges on request; Polymarket from September 2025)
Cost-Effective Storage – Optimized compression reduces costs
Batch Processing – Ideal for ETL pipelines and scheduled analytics
Data Warehousing – Perfect for building comprehensive warehouses

If you require low-latency or streaming blockchain data, Bitquery also provides:

Kafka Streams – Real-time blockchain data streams via Apache Kafka
GraphQL Subscriptions – Live data subscriptions for real-time applications
gRPC (CoreCast) – Low-latency gRPC streams, Solana only

For raw blocks to decode and parse your own way, see the Blockchain Data Lake.

Getting Started

Explore Sample Data – Review our GitHub repository to understand data structures
Choose Your Blockchain – Select from EVM, BSC, Solana, Pump.fun, Tron, Bitcoin, Ripple, Stellar, Polymarket, Robinhood, or Hyperliquid data exports
Set Up Cloud Storage – Configure AWS S3, Google Cloud Storage, or your preferred storage solution
Integrate Analytics Engine – Connect Snowflake, BigQuery, Athena, or your analytics platform
Build Your Pipeline – Create ETL jobs to process and transform blockchain data

EVM Data Export – Ethereum, Polygon, and other EVM chains
BSC Data Export – BNB Chain Data Store exports
Solana Data Export – Solana Data Store exports
Pump.fun Data Export – Pump.fun creations, migrations, trades, pools and OHLCV
Tron Data Export – Tron Data Store exports
Bitcoin Data Export – Bitcoin blockchain and OMNI data
Ripple Data Export – XRP Ledger transactions, payments, balances and ledger objects
Stellar Data Export – Stellar ledgers, operations, payments and effects
Polymarket Data Export – Polymarket prediction trades and settlements
Hyperliquid Data Export – HyperCore order book, fills, and L4 book diffs
Robinhood Data Export – Robinhood Chain trades, transfers, and tokenized equities
Kafka Streaming Concepts – Real-time blockchain data streams

Frequently Asked Questions

How do I get historical blockchain data in bulk?

Bitquery delivers Parquet datasets to AWS S3, Google Cloud Storage, Snowflake, or BigQuery — ideal for backfills, data lakes, and warehouse loads. See the chain-specific export pages linked above.

What file format are the Data Store exports?

Apache Parquet by default — columnar, compressed, and ready for Athena, Spark, BigQuery, or Snowflake. Other formats are available on request.

Can I sample the data before buying?

Yes. Explore schemas and sample files in the blockchain-cloud-data-dump-sample GitHub repo before buying on the Data Store or requesting a custom export.

How are Data Store exports different from the GraphQL API?

GraphQL is best for interactive queries and live apps. Data Store exports are for large-scale history, ETL pipelines, and teams that load data into their own warehouse.

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Overview​

Key Benefits​

Available Data Store Exports​

Data Format and Structure​

File Organization​

Sample Parquet Data​

Example: Ethereum Balance Updates​

Use Sample Data To:​

Cloud Platform Integration​

AWS S3 Integration​

Google Cloud Platform Integration​

Snowflake Integration​

Other Platforms​

Building Scalable Real-Time Solutions​

Real-Time Streaming Architecture​

Use Cases​

DeFi Analytics​

NFT Analytics​

Blockchain Forensics​

Data Science and Machine Learning​

Business Intelligence​

Real-Time vs Batch Data Access​

Getting Started​

Related Documentation​