Tron Data

Bitquery provides Tron 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.

Available Tron Topics

For Tron, Bitquery currently provides the following datasets:

• Blocks – Block-level metadata

• Transactions – Full transaction-level data

• Transfers – Native TRX and token transfers

• Balance Updates – Account balance changes per block

• DEX Trades – Executed trades on Tron DEXs

Sample Tron Cloud Dataset

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

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

Example Parquet file (public S3)

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

Tron Dataset Directory Structure

bitquery-blockchain-dataset/
└── tron/
    ├── balance_updates/
    │   ├── <start_block>_<end_block>.parquet
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── blocks/
    │   ├── <start_block>_<end_block>.parquet
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── dex_trades/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    ├── transactions/
    │   ├── <start_block>_<end_block>.parquet
    │   └── ...
    └── transfers/
        ├── <start_block>_<end_block>.parquet
        └── ...

Block Range Naming Convention

Each Parquet file name follows this format:

<start_block>_<end_block>.parquet

Example:

78861100_78861149.parquet

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/tron/transactions/78861100_78861149.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/tron/transactions/78861100_78861149.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 Tron data, Bitquery also provides:

• Kafka streams

• GraphQL subscriptions