Stellar Data

Bitquery provides Stellar blockchain data dumps in Parquet format, designed for large-scale analytics, historical backfills, and data lake integrations. These datasets can be hosted directly in your own cloud storage (for example, AWS S3) and queried using engines like Snowflake, BigQuery, Athena, Spark, etc.

Stellar data is modelled around ledgers → transactions → operations → effects, so most topics carry the transaction and operation context alongside the topic-specific fields.

Available Stellar Topics

For Stellar, Bitquery currently provides the following datasets:

• Blocks – Ledger-level metadata (protocol version, base fee, base reserve, fee pool, total coins)

• Transactions – Full transaction-level data with fee account, memo, sequence, and time bounds

• Operations – Operation-level records with source account and operation details

• Payments – Payment and path-payment operations, including source and destination assets

• Transfers – Native XLM and issued-asset transfers with sender, receiver, and direction

• Effects – Ledger effects produced by operations

• Effect Arguments – Key/value arguments attached to each effect

• Balance Effects – Account balance changes per asset

• Trade Effects – DEX trades on the Stellar order book, with buy/sell assets and price

• Claimable Balance Effects – Claimable balance creation and claiming, with claimant and sponsor

• Liquidity Pool Effects – Liquidity pool deposits, withdrawals, and share changes

• Liquidity Pool Trade Effects – Trades executed against liquidity pools

Sample Stellar Cloud Dataset

You can explore schemas and validate your tooling using the public Stellar sample datasets:

GitHub reference (schemas & examples)
https://github.com/bitquery/blockchain-cloud-data-dump-sample/tree/main/Stellar

Example Parquet file (public S3)

https://bitquery-blockchain-dataset.s3.us-east-1.amazonaws.com/stellar/payments_tx/<block_range>.parquet

Sample Parquet downloads (public S3)

• Blocks – Download

• Transactions – Download

• Operations – Download

• Payments – Download

• Transfers – Download

• Trade Effects – Download

Stellar Dataset Directory Structure

bitquery-blockchain-dataset/
└── stellar/
    ├── balance_effects_tx/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── blocks/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── claimable_balance_effects/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── effect_arguments_tx/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── effects_tx/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── liquidity_pool_effects/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── liquidity_pool_trade_effects/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── operations_tx/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── payments_tx/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── trade_effects_tx/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── transactions/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    └── transfers_tx/
        ├── <start_block>_<end_block>.parquet
        └── ...

Block Range Naming Convention

Each Parquet file name follows this format:

<start_block>_<end_block>.parquet

Example:

55080300_55080349.parquet

Here block is the Stellar ledger sequence number.

Common Fields

Most Stellar topics share the same transaction and operation context columns, which makes joining across topics straightforward:

• block, tx_date, tx_time – ledger sequence, date partition, and ledger close time

• tx_hash, tx_hash_bin, tx_index, transaction_index – transaction identity

• operation, op_index, operation_index, operation_name, op_source_account – operation identity

• effect, effect_index, order – effect identity on the effect-based topics

• *_annotation fields – Bitquery address labels, empty when the address is unlabelled

• Asset columns are prefixed per role: currency_from_* / currency_to_* on payments and transfers, buy_currency_* / sell_currency_* on trade effects

Get Full Access

The full dataset is delivered into your own cloud storage (S3, GCS) or warehouse share (Snowflake, BigQuery). To buy or trial it, fill the API form or contact sales@bitquery.io.

Reading Files with DuckDB

No key, no client library — point DuckDB at the public sample directly:

SELECT *
FROM read_parquet('https://bitquery-blockchain-dataset.s3.us-east-1.amazonaws.com/stellar/blocks/55080300_55080349.parquet')
LIMIT 10;

Reading Files in Python

import pandas as pd

df = pd.read_parquet("https://bitquery-blockchain-dataset.s3.us-east-1.amazonaws.com/stellar/blocks/55080300_55080349.parquet")
print(df.info())
df.head()

Real-Time vs Batch Data Access

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

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

• Kafka streams

• GraphQL subscriptions