AWS S3 - Cronos EVM Public Dataset

Cronos Public Dataset - AWS S3 Access Guide

The Cronos public blockchain dataset is hosted on Amazon S3 and is freely queryable via Athena, ClickHouse, Presto/Trino, DuckDB, or any engine that supports S3-backed Parquet or CSV data.

Dataset Overview

Base S3 path:

s3://aws-public-blockchain/v1.1/cronos/evm/

Public index page: https://aws-public-blockchain.s3.us-east-2.amazonaws.com/index.html#v1.1/cronos/evm/

Region: us-east-2 Update frequency: Nightly sync at +2 UTC

Available Tables

Table

Description

blocks

One row per block — includes block metadata like hash, miner, and gas usage.

transactions

One row per transaction — includes sender, recipient, gas, and value.

receipts

One row per transaction receipt — includes status, gas used, and logs count.

logs

One row per emitted EVM log — includes address, topics, and data.

decoded_events

Human-readable decoded events (topics and data mapped to ABI definitions).

Common Prerequisites (Step 0)

Before you start, make sure you:

Have internet access (dataset is public, no credentials needed).
Use region us-east-2 for AWS-based tools.
Have a basic understanding of SQL (optional).

Option 1: Amazon Athena (Console Method)

Step 0 — Create or Log In to AWS Account

Step 1 — Open Athena in the `us-east-2` Region

Go to https://console.aws.amazon.com/athena/.

Step 2 — Create a Database

CREATE DATABASE IF NOT EXISTS cronos;
USE cronos;

Step 3 — Create Tables

blocks

CREATE EXTERNAL TABLE IF NOT EXISTS blocks (
  block_hash          string,
  block_number        bigint,
  block_timestamp     bigint,
  parent_hash         string,
  gas_limit           bigint,
  gas_used            bigint,
  miner               string,
  size                bigint,
  extra_data          string,
  base_fee_per_gas    bigint,
  logs_bloom          string,
  state_root          string,
  transactions_root   string,
  receipts_root       string
)
PARTITIONED BY (date string)
STORED AS PARQUET
LOCATION 's3://aws-public-blockchain/v1.1/cronos/evm/'
TBLPROPERTIES (
  'parquet.compression'='SNAPPY',
  'projection.enabled'='true',
  'projection.date.type'='date',
  'projection.date.format'='yyyy-MM-dd',
  'projection.date.range'='2021-11-01,NOW',
  'storage.location.template'='s3://aws-public-blockchain/v1.1/cronos/evm/blocks/date=${date}/'
);

transactions

CREATE EXTERNAL TABLE IF NOT EXISTS transactions (
  block_hash                 string,
  block_number               bigint,
  block_timestamp            bigint,
  transaction_hash           string,
  transaction_index          int,
  nonce                      string,
  from_address               string,
  to_address                 string,
  value                      string,
  input                      string,
  gas                        string,
  gas_price                  string,
  transaction_type           tinyint
)
PARTITIONED BY (date string)
STORED AS PARQUET
LOCATION 's3://aws-public-blockchain/v1.1/cronos/evm/'
TBLPROPERTIES (
  'parquet.compression'='SNAPPY',
  'projection.enabled'='true',
  'projection.date.type'='date',
  'projection.date.format'='yyyy-MM-dd',
  'projection.date.range'='2021-11-08,NOW',
  'storage.location.template'='s3://aws-public-blockchain/v1.1/cronos/evm/transactions/date=${date}/'
);

receipts

CREATE EXTERNAL TABLE IF NOT EXISTS receipts (
  block_hash            string,
  block_number          bigint,
  block_timestamp       bigint,
  transaction_hash      string,
  transaction_index     int,
  from_address          string,
  to_address            string,
  contract_address      string,
  cumulative_gas_used   string,
  gas_used              string,
  effective_gas_price   string,
  status                tinyint
)
PARTITIONED BY (date string)
STORED AS PARQUET
LOCATION 's3://aws-public-blockchain/v1.1/cronos/evm/'
TBLPROPERTIES (
  'parquet.compression'='SNAPPY',
  'projection.enabled'='true',
  'projection.date.type'='date',
  'projection.date.format'='yyyy-MM-dd',
  'projection.date.range'='2021-11-08,NOW',
  'storage.location.template'='s3://aws-public-blockchain/v1.1/cronos/evm/receipts/date=${date}/'
);

logs

CREATE EXTERNAL TABLE IF NOT EXISTS logs (
  block_hash            string,
  block_number          bigint,
  block_timestamp       bigint,
  transaction_hash      string,
  transaction_index     int,
  log_index             int,
  address               string,
  data                  string,
  topics                array<string>,
  removed               boolean
)
PARTITIONED BY (date string)
STORED AS PARQUET
LOCATION 's3://aws-public-blockchain/v1.1/cronos/evm/'
TBLPROPERTIES (
  'parquet.compression'='SNAPPY',
  'projection.enabled'='true',
  'projection.date.type'='date',
  'projection.date.format'='yyyy-MM-dd',
  'projection.date.range'='2021-11-08,NOW',
  'storage.location.template'='s3://aws-public-blockchain/v1.1/cronos/evm/logs/date=${date}/'
);

decoded_events

CREATE EXTERNAL TABLE IF NOT EXISTS decoded_events (
  block_hash            string,
  block_number          bigint,
  block_timestamp       bigint,
  transaction_hash      string,
  transaction_index     int,
  log_index             int,
  address               string,
  event_hash            string,
  event_signature       string,
  topics                array<string>,
  args                  array<struct<key:string,value:string>>,
  removed               boolean
)
PARTITIONED BY (date string)
STORED AS PARQUET
LOCATION 's3://aws-public-blockchain/v1.1/cronos/evm/'
TBLPROPERTIES (
  'parquet.compression'='SNAPPY',
  'projection.enabled'='true',
  'projection.date.type'='date',
  'projection.date.format'='yyyy-MM-dd',
  'projection.date.range'='2021-11-08,NOW',
  'storage.location.template'='s3://aws-public-blockchain/v1.1/cronos/evm/decoded-events/date=${date}/'
);

Step 4 — Query Examples

SELECT COUNT(*) FROM blocks;
SELECT * FROM transactions WHERE to_address IS NOT NULL LIMIT 10;
SELECT event_name, COUNT(*) FROM decoded_events GROUP BY 1 ORDER BY 2 DESC;

Option 2: ClickHouse

Step 0 — Install ClickHouse

docker run -d \
  --name clickhouse-server \
  -p 8123:8123 -p 9000:9000 \
  -e CLICKHOUSE_PASSWORD='YOUR_PASSWORD' \
  clickhouse/clickhouse-server

brew install clickhouse

Step 1 — Connect

clickhouse-client

Step 2 — Query Public Parquet Data

SELECT block_number, miner
FROM s3(
  'https://aws-public-blockchain.s3.us-east-2.amazonaws.com/v1.1/cronos/evm/blocks/date=*/*.parquet',
  'Parquet'
)
WHERE block_number = 20000000
ORDER BY block_number DESC;

SELECT block_number, miner
FROM s3(
  'https://aws-public-blockchain.s3.us-east-2.amazonaws.com/v1.1/cronos/evm/blocks/date=2025-01-01/*.parquet',
  'Parquet'
)
ORDER BY block_number DESC
LIMIT 5;

Repeat similarly for other tables by adjusting the path:

/transactions/date=*/*.parquet
/receipts/date=*/*.parquet
/logs/date=*/*.parquet
/decoded_events/date=*/*.parquet

No credentials or setup required.

Option 3: Presto / Trino

Step 0 — Install

Follow: https://trino.io/docs/current/installation.html

Step 1 — Configure Hive Connector

Edit etc/catalog/hive.properties:

connector.name=hive
hive.s3.path-style-access=true
hive.s3.region=us-east-2

Step 2 — Start Trino

bin/launcher start

Step 3 — Create Schema & Tables

blocks

CREATE SCHEMA IF NOT EXISTS cronos;
USE cronos;

CREATE TABLE blocks (
  block_hash VARCHAR,
  block_number BIGINT,
  block_timestamp BIGINT,
  parent_hash VARCHAR,
  gas_limit BIGINT,
  gas_used BIGINT,
  miner VARCHAR,
  size BIGINT,
  extra_data VARCHAR,
  base_fee_per_gas BIGINT,
  logs_bloom VARCHAR,
  state_root VARCHAR,
  transactions_root VARCHAR,
  receipts_root VARCHAR,
  date VARCHAR
)
WITH (
  external_location = 's3a://aws-public-blockchain/v1.1/cronos/evm/blocks/',
  format = 'PARQUET',
  partitioned_by = ARRAY['date']
);

transactions

CREATE TABLE transactions (
  block_hash VARCHAR,
  block_number BIGINT,
  block_timestamp BIGINT,
  transaction_hash VARCHAR,
  transaction_index INTEGER,
  nonce VARCHAR,
  from_address VARCHAR,
  to_address VARCHAR,
  value VARCHAR,
  input VARCHAR,
  gas VARCHAR,
  gas_price VARCHAR,
  transaction_type TINYINT,
  date VARCHAR
)
WITH (
  external_location = 's3a://aws-public-blockchain/v1.1/cronos/evm/transactions/',
  format = 'PARQUET',
  partitioned_by = ARRAY['date']
);

receipts

CREATE TABLE receipts (
  block_hash VARCHAR,
  block_number BIGINT,
  block_timestamp BIGINT,
  transaction_hash VARCHAR,
  transaction_index INTEGER,
  from_address VARCHAR,
  to_address VARCHAR,
  contract_address VARCHAR,
  cumulative_gas_used VARCHAR,
  gas_used VARCHAR,
  effective_gas_price VARCHAR,
  status TINYINT,
  date VARCHAR
)
WITH (
  external_location = 's3a://aws-public-blockchain/v1.1/cronos/evm/receipts/',
  format = 'PARQUET',
  partitioned_by = ARRAY['date']
);

logs

CREATE TABLE logs (
  block_hash VARCHAR,
  block_number BIGINT,
  block_timestamp BIGINT,
  transaction_hash VARCHAR,
  transaction_index INTEGER,
  log_index INTEGER,
  address VARCHAR,
  data VARCHAR,
  topics ARRAY<VARCHAR>,
  removed BOOLEAN,
  date VARCHAR
)
WITH (
  external_location = 's3a://aws-public-blockchain/v1.1/cronos/evm/logs/',
  format = 'PARQUET',
  partitioned_by = ARRAY['date']
);

decoded events

CREATE TABLE decoded_events (
  block_hash VARCHAR,
  block_number BIGINT,
  block_timestamp BIGINT,
  transaction_hash VARCHAR,
  transaction_index INTEGER,
  log_index INTEGER,
  address VARCHAR,
  event_hash VARCHAR,
  event_signature VARCHAR,
  topics ARRAY<VARCHAR>,
  args ARRAY<ROW(key VARCHAR, value VARCHAR)>,
  removed BOOLEAN,
  date VARCHAR
)
WITH (
  external_location = 's3a://aws-public-blockchain/v1.1/cronos/evm/decoded-events/',
  format = 'PARQUET',
  partitioned_by = ARRAY['date']
);

Step 4 — Query

SELECT COUNT(*) FROM blocks;
SELECT * FROM transactions WHERE date = '2025-10-08' LIMIT 5;
SELECT block_number, COUNT(*) FROM receipts GROUP BY block_number ORDER BY block_number DESC LIMIT 10;

Option 4: DuckDB (Local)

Step 0 — Install

pip install duckdb

Step 1 — Run Query (CLI)

duckdb -c "
SELECT block_number, miner
FROM read_parquet('https://aws-public-blockchain.s3.us-east-2.amazonaws.com/v1.1/cronos/evm/blocks/date=2025-01-01/*.parquet')
ORDER BY block_number DESC
LIMIT 5;
"

Step 2 — Python Example

import duckdb
con = duckdb.connect()
con.sql("""
  SELECT block_number, miner
  FROM read_parquet('https://aws-public-blockchain.s3.us-east-2.amazonaws.com/v1.1/cronos/evm/blocks/date=2025-01-01/*.parquet')
  ORDER BY block_number DESC
  LIMIT 5;
""").show()

Works out-of-the-box — no AWS setup needed.

Option 5: Other Tools

Tool

Connection Example

Spark

spark.read.parquet("s3a://aws-public-blockchain/v1.1/cronos/evm/transactions/date=*/")

Dask / Polars

dd.read_parquet("https://.../*.parquet") or pl.scan_parquet()

AWS Glue

Create crawler with s3://aws-public-blockchain/v1.1/cronos/evm/

Redshift Spectrum

Create external schema and map Parquet tables to S3 paths

Summary

Engine

Auth Needed

Works With

Notes

Athena

AWS Console

Easiest for browser users

ClickHouse

Local or Cloud

Fast columnar queries

Presto / Trino

Cluster setup

Integrates with Hive

DuckDB

Local, Python

Lightweight and portable

Data Model — Cronos Public Dataset

The Cronos dataset consists of five interrelated tables:

blocks
│
├── transactions
│     └── receipts
│          └── logs
│               └── decoded_events

Each layer represents a deeper level of blockchain execution — from the block level down to decoded smart contract events.

Relationship Diagram (Text-Based)

+----------------------+
|       blocks         |
|----------------------|
| block_number (PK)    |
| block_hash           |
| block_timestamp      |
+----------------------+
           │
           │ 1 : N
           ▼
+----------------------+
|    transactions      |
|----------------------|
| transaction_hash (PK)|
| block_number (FK)    |
| from_address         |
| to_address           |
+----------------------+
           │
           │ 1 : 1
           ▼
+----------------------+
|      receipts        |
|----------------------|
| transaction_hash (PK)|
| block_number (FK)    |
| status               |
| gas_used             |
+----------------------+
           │
           │ 1 : N
           ▼
+----------------------+
|        logs          |
|----------------------|
| transaction_hash (FK)|
| log_index (PK)       |
| address              |
| topics               |
| data                 |
+----------------------+
           │
           │ 1 : 1
           ▼
+----------------------+
|   decoded_events     |
|----------------------|
| transaction_hash (FK)|
| log_index (FK)       |
| event_signature      |
| args (key/value)     |
+----------------------+

Key Relationships

Parent

Child

Join Columns

Relationship

blocks

transactions

block_number

One block has many transactions

transactions

receipts

transaction_hash

One-to-one

transactions

logs

transaction_hash

One-to-many

logs

decoded_events

transaction_hash, log_index

One-to-one

blocks

receipts / logs / decoded_events

block_number

Cross-layer link for time context

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hashtagDataset Overview

hashtagAvailable Tables

hashtagCommon Prerequisites (Step 0)

hashtagOption 1: Amazon Athena (Console Method)

hashtagStep 0 — Create or Log In to AWS Account

hashtagStep 1 — Open Athena in the us-east-2 Region

hashtagStep 2 — Create a Database

hashtagStep 3 — Create Tables

hashtagStep 4 — Query Examples

hashtagOption 2: ClickHouse

hashtagStep 0 — Install ClickHouse

hashtagStep 1 — Connect

hashtagStep 2 — Query Public Parquet Data

hashtagOption 3: Presto / Trino

hashtagStep 0 — Install

hashtagStep 1 — Configure Hive Connector

hashtagStep 2 — Start Trino

hashtagStep 3 — Create Schema & Tables

hashtagStep 4 — Query

hashtagOption 4: DuckDB (Local)

hashtagStep 0 — Install

hashtagStep 1 — Run Query (CLI)

hashtagStep 2 — Python Example

hashtagOption 5: Other Tools

hashtagSummary

hashtagData Model — Cronos Public Dataset

hashtagRelationship Diagram (Text-Based)

hashtagKey Relationships

Dataset Overview

Available Tables

Common Prerequisites (Step 0)

Option 1: Amazon Athena (Console Method)

Step 0 — Create or Log In to AWS Account

Step 1 — Open Athena in the `us-east-2` Region

Step 2 — Create a Database

Step 3 — Create Tables

Step 4 — Query Examples

Option 2: ClickHouse

Step 0 — Install ClickHouse

Step 1 — Connect

Step 2 — Query Public Parquet Data

Option 3: Presto / Trino

Step 0 — Install

Step 1 — Configure Hive Connector

Step 2 — Start Trino

Step 3 — Create Schema & Tables

Step 4 — Query

Option 4: DuckDB (Local)

Step 0 — Install

Step 1 — Run Query (CLI)

Step 2 — Python Example

Option 5: Other Tools

Summary

Data Model — Cronos Public Dataset

Relationship Diagram (Text-Based)

Key Relationships