Guide to Load Data from Azure Cloud to Snowflake

Introduction

In this blog , we are going to know about Loading data from Azure Blob Storage to Snowflake. This is a common ETL (Extract, Transform, Load) task for data engineers and analysts. This guide walk's you through the process using Snowflake SQL queries to seamlessly transfer a CSV file from Azure blob to Snowflake table.

Prerequisites

• A Snowflake account with appropriate permissions to create databases, stages, and tables.
• An Azure account with a Blob Storage container containing the data (e.g., students_dataset_50.csv).
• Azure tenant ID and Blob Storage URL for integration setup.
• Basic familiarity with Snowflake’s web interface
• An Azure account with an active subscription.
• Access to the Azure Portal (`portal.azure.com`).
• Basic familiarity with cloud interfaces.

Procedure

Step 1: Create a Database

First, create a database in Snowflake to store your data. The following command creates a database named raw_azure_db if it doesn’t already exist.

Use default schema or create a separate schema using. Here I’m using the default schema “PUBLIC” as my schema.

Procedure to create azure account:

Step 1.1: Sign In to the Azure Portal

• Go to [portal.azure.com].
• Sign in with your Azure account credentials.
• - If you’re new, follow the prompts to set up a free trail account.

Step 1.2: Create a Resource Group

A resource group is a logical container for Azure resources. Let’s create one for our storage account.

• 1. In the Azure Portal, click Create a resource (top-left corner) or search for Resource groups.
• Click Create.
• Fill in the details:
  
  - Subscription: Select your Azure subscription.
  
  - Resource group: Enter a name (e.g., myazure ).
  
  - Region: Choose a region (e.g., East US).



• Click Review + create, then Create.

Step 1.3: Create a Storage Account

The storage account provides a unique namespace for your Blob Storage data.

• In the Azure Portal, search for Storage accounts and select it.
• Click Create.
• Fill in the details:
  
  -Subscription: Select your subscription.
  
  -Resource group: Select created group name (e.g: ‘myazure’)
  
  -Storage account name: Enter a unique name (e.g., mystorageaccount2159).

  Must be lowercase, 3–24 characters, letters, and numbers only.

  -Region: Choose the same region as your resource group (e.g., East US).
  
  -Performance: Select Standard (sufficient for most use cases).
  
  -Redundancy: Choose Locally-redundant storage (LRS) for cost-efficiency.
  
  
• Click Review + create, then Create.
• Once created, click Go to resource to view the storage account.

common Name: mystorageaccount2159 (unique, descriptive, and aligns with your blobstorage2159 example).

Step 1.4: upload the data(azure):

Go to storage accounts And click refresh and click the created storage account and click the upload button on top.then browse the files and uploaded

Step 2: Create a Storage Integration

To connect Snowflake with Azure Blob Storage, create a storage integration. This defines the connection parameters, including the Azure tenant ID and allowed storage locations.

After creating the integration, describe it to verify the setup:

Authorize Snowflake Using AZURE_CONSENT_URL

After creating the storage integration, you need to authorize Snowflake to access your Azure Blob Storage. Here’s how

Copy the azure_content_url from the output

• Access the URL : Paste the URL into a browser and sign in with an Azure account that has admin privileges in your tenant.
• Grant Permissions : Review the permissions requested by Snowflake (e.g., access to your Blob Storage). Click Accept to authorize

Note: Ensure the Azure tenant ID and storage URL match your Azure account details. You may need to grant Snowflake access to the Azure storage account via role-based access control (RBAC).

Step 3: Create a File Format

Define a file format to specify how Snowflake should parse the CSV file. Here, we create a CSV file format that skips the header row and uses a comma as the delimiter.

Adjust the SKIP_HEADER or FIELD_DELIMITER based on your CSV file’s structure.

Step 3: Create a File Format

Define a file format to specify how Snowflake should parse the CSV file. Here, we create a CSV file format that skips the header row and uses a comma as the delimiter.

Adjust the SKIP_HEADER or FIELD_DELIMITER based on your CSV file’s structure.

Step 4: Create a Stage

A stage in Snowflake is a reference to the external storage location (Azure Blob Storage). The stage links to the storage integration and specifies the file format.

Step 5: Verify Stage Contents

Preview the data in the stage to confirm the file is accessible and formatted correctly.

This query displays the raw contents of the CSV file as a single column. Check for any parsing issues here.

Step 6: Create a Target Table

Create a table in Snowflake to store the data from the CSV file. The table’s schema should match the CSV file’s structure.

Ensure the column names and data types align with the CSV file’s columns.

Load Data into the Table

Use the COPY INTO command to load the data from the Azure stage into the STUDENTS2 table. The ON_ERROR = CONTINUE option ensures the process continues even if some rows fail to load.

Step 8: Verify the Loaded Data

Query the table to confirm the data was loaded successfully.

Summary

You’ve now loaded a CSV file from Azure Blob Storage into a Snowflake table! The process involves setting up a database, connecting Snowflake to Azure via a storage integration, defining a file format, staging the data, and loading it into a table. If you encounter issues, verify the Azure URL, file format, and column mappings. For large datasets, consider Snowflake’s parallel loading capabilities to optimize performance.