Skip to main content

​
Create Azure Machine Learning Datasets from Azure Open Datasets

Learn how to bring curated enrichment data into your local or remote machine learning experiments with Azure Machine Learning datasets and Azure Open Datasets.

​
Overview

With an Azure Machine Learning dataset, you create a reference to the data source location, along with a copy of its metadata. Because datasets are lazily evaluated, and because the data remains in its existing location, you:
  • Don’t risk unintentionally changing your original data sources
  • Incur no extra storage cost
  • Improve ML workflow performance speeds

​
Prerequisites

You need: 
pip install azureml-opendatasets
Some dataset classes have dependencies on the azureml-dataprep package, which is only compatible with 64-bit Python.For Linux users, these classes are supported only on: Debian (8, 9), Fedora (27, 28), Red Hat Enterprise Linux (7, 8), and Ubuntu (14.04, 16.04, 18.04).

​
Create Datasets with the Python SDK

To create Azure Machine Learning datasets via Azure Open Datasets classes in the Python SDK, make sure you installed the package: 
pip install azureml-opendatasets

​
Return TabularDataset or FileDataset

Each discrete dataset is represented by a class, and certain classes are available as either:
  • FileDataset - references single or multiple files in datastores or public URLs
  • TabularDataset - represents data in a tabular format

​
Example: MNIST Dataset

The MNIST class can return either a TabularDataset or FileDataset: 
from azureml.core import Dataset
from azureml.opendatasets import MNIST

# MNIST class can return either TabularDataset or FileDataset
tabular_dataset = MNIST.get_tabular_dataset()
file_dataset = MNIST.get_file_dataset()

​
Example: Diabetes Dataset

The Diabetes class is only available as a TabularDataset: 
from azureml.opendatasets import Diabetes
from azureml.core import Dataset

# Diabetes class can return ONLY TabularDataset
diabetes_tabular = Diabetes.get_tabular_dataset()

​
Example: Public Holidays Dataset

Access worldwide public holiday data: 
from azureml.opendatasets import PublicHolidays
from datetime import datetime
from dateutil.relativedelta import relativedelta

# Get holidays from the last month
end_date = datetime.today()
start_date = datetime.today() - relativedelta(months=1)

hol = PublicHolidays(start_date=start_date, end_date=end_date)
hol_df = hol.to_pandas_dataframe()

# Display the data
print(hol_df.info())
print(hol_df.head())

​
Register Datasets

Register an Azure Machine Learning dataset with your workspace to share it with others and reuse it across experiments. When you register a dataset created from Open Datasets, no data is immediately downloaded, but the data becomes accessible later when requested from a central storage location. 
from azureml.core import Workspace

# Connect to your workspace
ws = Workspace.from_config()

# Register the dataset
titanic_ds = titanic_ds.register(
    workspace=ws,
    name='titanic_ds',
    description='titanic training data'
)

​
Create Datasets with Azure ML Studio

You can also create Azure Machine Learning datasets from Azure Open Datasets using the Azure Machine Learning studio interface.
Datasets created through Azure Machine Learning studio are automatically registered to the workspace.

​
Step 1: Navigate to Data Assets

In your workspace, select Data in the left navigation. At the Data assets tab, select Create.

​
Step 2: Configure Dataset Type

  1. Add a name and optional description for the new data asset
  2. Select Tabular in the Type dropdown
  3. Click Next

​
Step 3: Select Source

  1. Select From Azure Open Datasets
  2. Click Next

​
Step 4: Choose Dataset

Select an available Azure Open Dataset from the list. For example:
  • San Francisco Safety Data
  • US Labor Force Statistics
  • Public Holidays
  • NYC Taxi datasets

​
Step 5: Filter Data (Optional)

Optionally filter the data with available filters. For example, for the San Francisco Safety Data dataset, you can filter by date range: 
Start date: July 1, 2024
End date: July 17, 2024

​
Step 6: Review and Create

  1. Review the settings for the new data asset
  2. Make any necessary changes
  3. Select Create
The dataset is now available in your workspace under Datasets. You can use it in the same way as other datasets you created.

​
Direct Access with Azure Storage

You can also access datasets directly from Azure Blob Storage without the SDK: 
import pandas as pd

# Read directly from blob storage
df = pd.read_parquet(
    "https://azureopendatastorage.blob.core.windows.net/holidaydatacontainer/Processed/latest.parquet"
)

​
Using Azure Storage SDK

from azure.storage.blob import BlobServiceClient, ContainerClient
import os

# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
container_name = "holidaydatacontainer"
folder_name = "Processed"

# Create blob service client
container_url = f"https://{azure_storage_account_name}.blob.core.windows.net/"
blob_service_client = BlobServiceClient(container_url)

# Get container client
container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs(folder_name)

# Find target parquet file
for blob in blobs:
    if blob.name.endswith('.parquet'):
        print(f"Found: {blob.name}")
        break

​
Access Datasets in Azure Databricks

​
Using azureml-opendatasets

# Install in Databricks cluster first
# pip install azureml-opendatasets

from azureml.opendatasets import PublicHolidays
from datetime import datetime
from dateutil.relativedelta import relativedelta

end_date = datetime.today()
start_date = datetime.today() - relativedelta(months=1)

hol = PublicHolidays(start_date=start_date, end_date=end_date)
hol_df = hol.to_spark_dataframe()

display(hol_df.limit(5))

​
Using PySpark Direct Access

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "holidaydatacontainer"
blob_relative_path = "Processed"

# Configure Spark to read from Blob
wasbs_path = f"wasbs://{blob_container_name}@{blob_account_name}.blob.core.windows.net/{blob_relative_path}"

# Read parquet files
df = spark.read.parquet(wasbs_path)
df.createOrReplaceTempView('source')

# Query the data
display(spark.sql('SELECT * FROM source LIMIT 10'))

​
Use Datasets in ML Experiments

Once you’ve created and registered datasets, you can use them in your machine learning experiments: 
from azureml.core import Workspace, Dataset

# Get workspace
ws = Workspace.from_config()

# Get registered dataset
dataset = Dataset.get_by_name(ws, name='titanic_ds')

# Use in training script
df = dataset.to_pandas_dataframe()

# Or mount for file-based access
mount_context = dataset.mount()
mount_context.start()

​
Best Practices

Datasets use lazy evaluation - data is only loaded when needed. This improves performance and reduces memory usage.
Register datasets in your workspace for versioning, sharing, and reproducibility across experiments.
When using the SDK, filter data at query time rather than loading entire datasets into memory.
Use Parquet for large datasets and analytics workloads. Use CSV for compatibility with external tools.

​
Next Steps

Train Your First ML Model

Learn how to train models with datasets

Dataset Catalog

Browse all available datasets

Public Holidays Dataset

Explore the public holidays dataset

Python SDK Reference

View full SDK documentation

Build docs developers (and LLMs) love

Get started for freeTalk to us