Blockchain Data in Cloud

Bitquery provides ready-to-use blockchain data dumps 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

Our cloud data export service delivers 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 – Complete blockchain history from genesis blocks
• Multi-Chain Support – Access data from major blockchain networks
• Cost-Effective – Pay only for the data you need, when you need it
• Scalable – Handle petabytes of blockchain data with ease

Available Blockchain Data Dumps

Bitquery provides comprehensive cloud data dumps 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.

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.

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.

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.

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 legs, 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:

• Order Statuses – Full order lifecycle: open, canceled, filled, triggered, and every rejection reason
• Book Diffs – L4 per-order book deltas with the owning address and order id
• Fills – Both sides of every match, with realized PnL, fee, fee token, and liquidation detail
• Oracle Updates – Mark, spot, and external perp price inputs, and the resulting oracle prices
• Misc Events – Deposits, withdrawals, transfers, funding, delegation, validator rewards
• TWAP Statuses – TWAP order state, executed size and notional, terminal status
• Core Writer Actions – HyperEVM to HyperCore actions, with the originating EVM transaction hash

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

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.

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.

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 – Reduces storage costs by up to 90%
• 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/
│   ├── order_statuses/
│   ├── book_diffs/
│   ├── fills/
│   ├── oracle_updates/
│   └── ...
└── 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 dumps 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
• Easily request more files from the same bucket/prefix if you need additional data
• 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_24053999.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 that can handle millions of transactions and events per second. Our cloud-native architecture supports both batch and streaming data pipelines for comprehensive blockchain analytics.

Real-Time Streaming Architecture

Build low-latency applications with sub-second data delivery 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 – Sub-100ms data delivery for time-sensitive applications

Scalable Cloud Solutions

Design and deploy horizontally scalable solutions that can handle:

• High Throughput – Process millions of transactions per day
• Concurrent Users – Support thousands of simultaneous connections
• Data Volume – Handle petabytes of historical and real-time data
• Global Scale – Deploy across multiple cloud regions for low latency

Performance Benchmarks

Our cloud solutions support:

• Latency: Sub-100ms for real-time streams, sub-second for batch queries
• Throughput: Millions of transactions per second processing capability
• Scalability: Auto-scaling from zero to thousands of concurrent connections
• Availability: 99.9% uptime SLA with multi-region redundancy
• Data Freshness: Real-time data with <1 second delay from blockchain

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

Cloud data dumps are optimized for batch analytics and historical workloads. They provide:

• Complete Historical Data – Access to full blockchain history
• Cost-Effective Storage – Optimized compression reduces costs
• Batch Processing – Ideal for ETL pipelines and scheduled analytics
• Data Warehousing – Perfect for building comprehensive data lakes

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

Getting Started

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

Related Documentation

• EVM Data Export – Ethereum, Polygon, and other EVM chains
• BSC Data Export – BNB Chain blockchain data dumps
• Solana Data Export – Solana blockchain data dumps
• Tron Data Export – Tron blockchain data dumps
• Bitcoin Data Export – Bitcoin blockchain and OMNI data
• 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 cloud 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 requesting a full export.

How is cloud export different from the GraphQL API?

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