File Container - Datazone

Overview

File Containers allows you to manage and store files. Each project has its own isolated file container. You can interact with file containers like AWS S3 buckets or Google Cloud Storage buckets. Also, Datazone provides toolkits to interact with file containers. You can use the FileContainerClient in notebooks and pipelines to interact with file containers.

Click Projects in the left sidebar.
Choose a project from the card list.
Click File Containers tab in the left sidebar.

Client Usage

The FileContainerClient provides a convenient interface to interact with file containers using S3-compatible storage. It handles authentication and bucket management automatically.

The FileContainerClient is only available in the execution environment (pipelines and notebooks running on Datazone). For local development and external applications, see the Local Access section below.

You can access the FileContainerClient in your pipelines and notebooks like this:

from datazone import FileContainerClient

`list_objects`

Lists objects in the file container with an optional prefix filter.

from datazone import FileContainerClient

# List all objects
objects = FileContainerClient.list_objects()

# List objects with a specific prefix
documents = FileContainerClient.list_objects("documents/")

Parameters:

prefix (str): Optional prefix to filter objects by path

Returns:

list: List of object metadata dictionaries. Example:

[
    {
        'Key': 'customer_list.csv',
        'LastModified': datetime.datetime(2025, 7, 24, 19, 44, 12, 475000, tzinfo=tzlocal()),
        'ETag': '"bf36dc829c4229254b7df3c428d0a349"',
        'Size': 18311622,
        'StorageClass': 'STANDARD'
    }
]

`get_object`

Retrieves an object from the file container by its key.

# Get a file's content
file_data = FileContainerClient.get_object("data/sample.csv")

# Convert bytes to string for text files
content = file_data.decode('utf-8')

Parameters:

key (str): The key/path of the object to retrieve

Returns:

bytes: The object’s raw data

Objects are stored as bytes, so you may need to encode/decode text data appropriately

`put_object`

Stores data in the file container at the specified key.

# Store text data
text_data = "Hello, World!".encode('utf-8')
FileContainerClient.put_object("messages/hello.txt", text_data)

# Store binary data
with open("local_file.pdf", "rb") as f:
    file_data = f.read()
    FileContainerClient.put_object("documents/file.pdf", file_data)

Parameters:

key (str): The key/path where the object will be stored
data (bytes): The data to store

`delete_object`

Removes an object from the file container.

# Delete a specific file
FileContainerClient.delete_object("temp/old_file.txt")

Parameters:

key (str): The key/path of the object to delete

Examples

Periodically Uploading Files in a Pipeline

You can use the FileContainerClient to periodically upload files to your file container. This can be useful for tasks like logging, data collection, or backups.

from datazone import transform, FileContainerClient
import requests
from datetime import datetime
import io

@transform
def fetch_and_store_llm_data():
    # URL to fetch the data from
    url = "https://docs.datazone.co/llms-full.txt"
    
    # Get the current timestamp in ISO format
    timestamp_as_iso = datetime.now().isoformat()
    
    # Fetch the data from the URL
    response = requests.get(url)
    
    # Check if the request was successful
    if response.status_code == 200:
        # Get the content
        content = response.text
        
        # Store the content in the file system
        FileContainerClient.put_object(f"daily_llm/{timestamp_as_iso}/llm.txt", io.BytesIO(content.encode('utf-8')))
        
        return f"Successfully stored LLM data with timestamp {timestamp_as_iso}"
    else:
        raise Exception(f"Failed to fetch data: HTTP {response.status_code}")

Read a Parquet File in a Notebook

import io
import pandas as pd
from datazone import FileContainerClient

# Read a Parquet file from the file container
data = FileContainerClient.get_object("datasets/sample.parquet")

# Convert bytes to a Pandas DataFrame
df = pd.read_parquet(io.BytesIO(data))

Local Access

For local development and external applications, you can access File Containers using S3-compatible tools and SDKs. This requires Access Keys for authentication.

Prerequisites

Before connecting locally, you need:

Access Keys - Create from your project settings (Learn how)
Endpoint URL - Your Datazone instance URL (e.g., your-instance.datazone.co:3333)
Project Path - Format: {project-name}/main/file-container/

AWS CLI

Install AWS CLI

# macOS
brew install awscli

# Linux
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install

# Windows
msiexec.exe /i https://awscli.amazonaws.com/AWSCLIV2.msi

Configure Credentials

Set your access keys as environment variables:

export AWS_ACCESS_KEY_ID="your-access-key-id"
export AWS_SECRET_ACCESS_KEY="your-secret-access-key"

List Files

aws s3 ls --endpoint-url https://your-instance.datazone.co:3333 \
  your-project-bucket/main/file-container/

Upload a File

aws s3 cp local-file.txt \
  --endpoint-url https://your-instance.datazone.co:3333 \
  s3://your-project-bucket/main/file-container/local-file.txt

Download a File

aws s3 cp --endpoint-url https://your-instance.datazone.co:3333 \
  s3://your-project-bucket/main/file-container/remote-file.txt \
  local-file.txt

Sync Directory

# Upload entire directory
aws s3 sync ./local-folder \
  --endpoint-url https://your-instance.datazone.co:3333 \
  s3://your-project-bucket/main/file-container/remote-folder/

# Download entire directory
aws s3 sync --endpoint-url https://your-instance.datazone.co:3333 \
  s3://your-project-bucket/main/file-container/remote-folder/ \
  ./local-folder

Python (boto3)

Install boto3

pip install boto3

Configure S3 Client

import boto3

s3_client = boto3.client(
    's3',
    endpoint_url='https://your-instance.datazone.co:3333',
    aws_access_key_id='your-access-key-id',
    aws_secret_access_key='your-secret-access-key'
)

bucket_name = 'your-project-bucket'
prefix = 'main/file-container/'

List Files

response = s3_client.list_objects_v2(
    Bucket=bucket_name,
    Prefix=prefix
)

for obj in response.get('Contents', []):
    print(obj['Key'])

Upload a File

s3_client.upload_file(
    'local-file.txt',
    bucket_name,
    f'{prefix}local-file.txt'
)
print('File uploaded successfully')

Download a File

s3_client.download_file(
    bucket_name,
    f'{prefix}remote-file.txt',
    'local-file.txt'
)
print('File downloaded successfully')

Upload with Metadata

s3_client.upload_file(
    'data.csv',
    bucket_name,
    f'{prefix}data.csv',
    ExtraArgs={
        'Metadata': {
            'source': 'analytics',
            'date': '2026-01-20'
        }
    }
)

JavaScript (AWS SDK)

Install AWS SDK

npm install aws-sdk

Configure S3 Client

const AWS = require('aws-sdk');

const s3 = new AWS.S3({
  endpoint: 'https://your-instance.datazone.co:3333',
  accessKeyId: 'your-access-key-id',
  secretAccessKey: 'your-secret-access-key',
  s3ForcePathStyle: true,
  signatureVersion: 'v4'
});

const bucketName = 'your-project-bucket';
const prefix = 'main/file-container/';

List Files

s3.listObjectsV2({
  Bucket: bucketName,
  Prefix: prefix
}, (err, data) => {
  if (err) console.error(err);
  else {
    data.Contents.forEach(obj => {
      console.log(obj.Key);
    });
  }
});

Upload a File

const fs = require('fs');
const fileContent = fs.readFileSync('local-file.txt');

s3.putObject({
  Bucket: bucketName,
  Key: `${prefix}local-file.txt`,
  Body: fileContent
}, (err, data) => {
  if (err) console.error(err);
  else console.log('File uploaded successfully');
});

Download a File

s3.getObject({
  Bucket: bucketName,
  Key: `${prefix}remote-file.txt`
}, (err, data) => {
  if (err) console.error(err);
  else {
    fs.writeFileSync('local-file.txt', data.Body);
    console.log('File downloaded successfully');
  }
});

Java (AWS SDK)

Add Dependency

<!-- Maven -->
<dependency>
    <groupId>com.amazonaws</groupId>
    <artifactId>aws-java-sdk-s3</artifactId>
    <version>1.12.400</version>
</dependency>

Configure S3 Client

import com.amazonaws.auth.AWSStaticCredentialsProvider;
import com.amazonaws.auth.BasicAWSCredentials;
import com.amazonaws.client.builder.AwsClientBuilder;
import com.amazonaws.services.s3.AmazonS3;
import com.amazonaws.services.s3.AmazonS3ClientBuilder;
import com.amazonaws.services.s3.model.*;
import java.io.File;

BasicAWSCredentials credentials = new BasicAWSCredentials(
    "your-access-key-id",
    "your-secret-access-key"
);

AmazonS3 s3Client = AmazonS3ClientBuilder.standard()
    .withEndpointConfiguration(
        new AwsClientBuilder.EndpointConfiguration(
            "https://your-instance.datazone.co:3333",
            "us-east-1"
        )
    )
    .withCredentials(new AWSStaticCredentialsProvider(credentials))
    .withPathStyleAccessEnabled(true)
    .build();

String bucketName = "your-project-bucket";
String prefix = "main/file-container/";

List Files

ListObjectsV2Request listRequest = new ListObjectsV2Request()
    .withBucketName(bucketName)
    .withPrefix(prefix);

ListObjectsV2Result result = s3Client.listObjectsV2(listRequest);
for (S3ObjectSummary objectSummary : result.getObjectSummaries()) {
    System.out.println(objectSummary.getKey());
}

Upload a File

File file = new File("local-file.txt");
s3Client.putObject(
    bucketName,
    prefix + "local-file.txt",
    file
);
System.out.println("File uploaded successfully");

Download a File

S3Object s3Object = s3Client.getObject(
    bucketName,
    prefix + "remote-file.txt"
);
S3ObjectInputStream inputStream = s3Object.getObjectContent();
// Save to file or process the input stream
System.out.println("File downloaded successfully");

Common Operations

Check if File Exists

Python:

try:
    s3_client.head_object(Bucket=bucket_name, Key=f'{prefix}file.txt')
    print('File exists')
except:
    print('File does not exist')

Delete a File

AWS CLI:

aws s3 rm --endpoint-url https://your-instance.datazone.co:3333 \
  s3://your-project-bucket/main/file-container/file.txt

Python:

s3_client.delete_object(
    Bucket=bucket_name,
    Key=f'{prefix}file.txt'
)

Get File Metadata

Python:

response = s3_client.head_object(
    Bucket=bucket_name,
    Key=f'{prefix}file.txt'
)
print(f"Size: {response['ContentLength']} bytes")
print(f"Last Modified: {response['LastModified']}")

Best Practices

Use Environment Variables - Never hardcode credentials in code
Handle Errors - Always wrap operations in try-catch blocks
Stream Large Files - Use streaming uploads/downloads for large files
Set Timeouts - Configure appropriate timeouts for your use case
Clean Up - Delete temporary files after processing
Monitor Usage - Track file operations for cost management

Get Started

Reference

Deployment

Tutorial

​Overview

​Client Usage

​list_objects

​get_object

​put_object

​delete_object

​Examples

​Periodically Uploading Files in a Pipeline

​Read a Parquet File in a Notebook

​Local Access

​Prerequisites

​AWS CLI

​Install AWS CLI

​Configure Credentials

​List Files

​Upload a File

​Download a File

​Sync Directory

​Python (boto3)

​Install boto3

​Configure S3 Client

​List Files

​Upload a File

​Download a File

​Upload with Metadata

​JavaScript (AWS SDK)

​Install AWS SDK

​Configure S3 Client

​List Files

​Upload a File

​Download a File

​Java (AWS SDK)

​Add Dependency

​Configure S3 Client

​List Files

​Upload a File

​Download a File

​Common Operations

​Check if File Exists

​Delete a File

​Get File Metadata

​Best Practices

​Next Steps

Overview

Client Usage

`list_objects`

`get_object`

`put_object`

`delete_object`

Examples

Periodically Uploading Files in a Pipeline

Read a Parquet File in a Notebook

Local Access

Prerequisites

AWS CLI

Install AWS CLI

Configure Credentials

List Files

Upload a File

Download a File

Sync Directory

Python (boto3)

Install boto3

Configure S3 Client

List Files

Upload a File

Download a File

Upload with Metadata

JavaScript (AWS SDK)

Install AWS SDK

Configure S3 Client

List Files

Upload a File

Download a File

Java (AWS SDK)

Add Dependency

Configure S3 Client

List Files

Upload a File

Download a File

Common Operations

Check if File Exists

Delete a File

Get File Metadata

Best Practices

Next Steps