Load data from cloud storage: Microsoft Azure¶

Introduction¶

This tutorial shows you how to load data from cloud storage into Snowflake using SQL. You use a template worksheet in Snowsight to complete these tasks. You can choose which cloud provider you want to use: Amazon S3, Microsoft Azure, or Google Cloud Storage (GCS). The worksheet contains customized SQL commands for compatibility with each type of storage.

Prerequisites¶

This tutorial assumes the following:

• You have a supported browser.

• You have a trial account. If you do not have a trial account yet, you can sign up for a free trial. You can choose any Snowflake Cloud Region.

• You have an account that you can use to bulk load data from one of the following cloud providers:

  • AWS S3. See Bulk loading from Amazon S3.

  • Microsoft Azure. See Bulk loading from Microsoft Azure.

  • Google Cloud Storage. See Bulk loading from Google Cloud Storage.

Step 1. Sign in using Snowsight¶

To access Snowsight over the public Internet, do the following:

1. In a supported web browser, navigate to https://app.snowflake.com.

2. Provide your account identifier or account URL. If you’ve previously signed in to Snowsight, you might see an account name that you can select.

3. Sign in using your Snowflake account credentials.

Step 2. Open the Load data from cloud storage worksheet¶

You can use worksheets to write and run SQL commands on your database. Your trial account has access to a template worksheet for this tutorial. The worksheet has the SQL commands that you will run to create database objects, load data, and query the data. Because it is a template worksheet, you will be invited to enter your own values for certain SQL parameters. For more information about worksheets, see Getting started with worksheets.

The worksheet for this tutorial is not pre-loaded into the trial account. To open the worksheet for this tutorial, follow these steps:

1. If you are signing in to your Snowsight trial account for the first time, select Start under Load data into Snowflake on the Where do you want to start? screen.

   If you have left the Where do you want to start? screen, go to the Worksheets tab and select Continue in the banner.

2. Click anywhere in the middle panel named Load data from cloud storage.

   The [Template] Load data from cloud storage worksheet opens, and your browser looks similar to the following image.

Step 3. Set the role and warehouse to use¶

The role you use determines the privileges you have. In this tutorial, use the ACCOUNTADMIN system role so that you can view and manage objects in your account. For more information, see Using the ACCOUNTADMIN Role.

A warehouse provides the compute resources that you need to execute DML operations, load data, and run queries. These resources include CPU, memory, and temporary storage. Your trial account has a virtual warehouse (compute_wh) that you can use for this tutorial. For more information, see Virtual warehouses.

To set the role and warehouse to use, do the following:

1. In the open worksheet, place your cursor in the USE ROLE line.

   USE ROLE accountadmin;
   

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2. In the upper-right corner of the worksheet, select Run.

   Note

   In this tutorial, run SQL statements one at a time. Do not select Run All.

3. Place your cursor in the USE WAREHOUSE line, then select Run.

   USE WAREHOUSE compute_wh;
   

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Step 4. Set up a table that you can load¶

A database is a repository for your data. The data is stored in tables that you can manage and query. A schema is a logical grouping of database objects, such as tables and views. For example, a schema might contain the database objects required for a specific application. For more information, see Databases, Tables and Views - Overview.

To create a database, a schema, and a table that you can load, do the following:

1. In the open worksheet, place your cursor in the CREATE OR REPLACE DATABASE line, enter a name for your database and an optional comment, then select Run. For example:

   CREATE OR REPLACE DATABASE cloud_data_db
     COMMENT = 'Database for loading cloud data';
   

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2. Place your cursor in the CREATE OR REPLACE SCHEMA line, enter a name for your schema and an optional comment, then select Run. For example:

   CREATE OR REPLACE SCHEMA cloud_data_db.azure_data
     COMMENT = 'Schema for tables loaded from Azure';
   

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3. Place your cursor in the CREATE OR REPLACE TABLE lines, complete the table definition, add an optional comment, and select Run. For example, the following table contains six columns:

   CREATE OR REPLACE TABLE cloud_data_db.azure_data.calendar
     (
     full_date DATE
     ,day_name VARCHAR(10)
     ,month_name VARCHAR(10)
     ,day_number VARCHAR(2)
     ,full_year VARCHAR(4)
     ,holiday BOOLEAN
     )
     COMMENT = 'Table to be loaded from Azure calendar data file';
   

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4. To confirm that the table was created successfully, place your cursor in the SELECT line, then select Run.

   SELECT * FROM cloud_data_db.azure_data.calendar;
   

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   The output shows the columns of the table you created. Currently, the table does not have any rows.

Step 5. Create a storage integration¶

Before you can load data from cloud storage, you must configure a storage integration that is specific to your cloud provider. The following example is specific to Microsoft Azure storage.

Storage integrations are named, first-class Snowflake objects that avoid the need for passing explicit cloud provider credentials such as secret keys or access tokens. Integration objects store an Azure identity and access management (IAM) user ID called the app registration.

To create a storage integration for Azure, do the following:

1. Use the Azure portal to configure an Azure container for loading data. For details, see Configuring an Azure container for loading data.

2. In the open worksheet, place your cursor in the CREATE OR REPLACE STORAGE INTEGRATION lines, define the required parameters, and select Run. For example:

   CREATE OR REPLACE STORAGE INTEGRATION azure_data_integration
     TYPE = EXTERNAL_STAGE
     STORAGE_PROVIDER = 'AZURE'
     AZURE_TENANT_ID = '075f576e-6f9b-4955-8e99-4086736225d9'
     ENABLED = TRUE
     STORAGE_ALLOWED_LOCATIONS = ('azure://tutorial99.blob.core.windows.net/snow-tutorial-container/');
   

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   Set AZURE_TENANT_ID to the Office 365 tenant ID for the storage account that contains the allowed storage locations that you want to use. You can find this ID in the Azure portal under Microsoft Entra ID > Properties > Tenant ID. (Microsoft Entra ID is the new name for Azure Active Directory.)

   Set STORAGE_ALLOWED_LOCATIONS to the path for the Azure container where your source data file is stored. Use the format shown in this example, where tutorial99 is the storage account name and snow-tutorial-container is the container name.

3. Place your cursor in the DESCRIBE INTEGRATION line, specify the name of the storage integration you created, and select Run.

   DESCRIBE INTEGRATION azure_data_integration;
   

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   This command retrieves the AZURE_CONSENT_URL and AZURE_MULTI_TENANT_APP_NAME for the client application that was created automatically for your Snowflake account. You will use these values to configure permissions for Snowflake in the Azure portal.

   The output for this command looks similar to the following:

   

4. Place your cursor in the SHOW INTEGRATIONS line and select Run. This command returns information about the storage integration you created.

   SHOW INTEGRATIONS;
   

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   The output for this command looks similar to the following:

   

5. Use the Azure portal to configure permissions for the client application (which was created automatically for your trial account) to access storage containers. Follow Step 2: Grant Snowflake Access to the Storage Locations under Configuring an Azure container for loading data.

Step 6. Create a stage¶

A stage is a location that holds data files to load into a Snowflake database. This tutorial creates a stage that can load data from a specific type of cloud storage, such as an Azure container.

To create a stage, do the following:

1. In the open worksheet, place your cursor in the CREATE OR REPLACE STAGE lines, specify a name, the storage integration you created, the bucket URL, and the correct file format, then select Run. For example:

   CREATE OR REPLACE STAGE cloud_data_db.azure_data.azuredata_stage
     STORAGE_INTEGRATION = azure_data_integration
     URL = 'azure://tutorial99.blob.core.windows.net/snow-tutorial-container/'
     FILE_FORMAT = (TYPE = CSV);
   

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2. Return information about the stage you created:

   SHOW STAGES;
   

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   The output for this command looks similar to the following:

   

Step 7. Load data from the stage¶

Load the table from the stage you created by using the COPY INTO <table> command. For more information about loading from Azure containers, see Copying data from an Azure stage.

To load the data into the table, place your cursor in the COPY INTO lines, specify the table name, the stage you created, and name of the file (or files) you want to load, then select Run. For example:

COPY INTO cloud_data_db.azure_data.calendar
  FROM @cloud_data_db.azure_data.azuredata_stage
    FILES = ('calendar.txt');

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Your output looks similar to the following image.

Step 8. Query the table¶

Now that the data is loaded, you can run queries on the calendar table.

To run a query in the open worksheet, select the line or lines of the SELECT command, and then select Run. For example, run the following query:

SELECT * FROM cloud_data_db.azure_data.calendar;

Your output looks similar to the following image.

Step 9. Cleanup, summary, and additional resources¶

Congratulations! You have successfully completed this tutorial for trial accounts.

Take a few minutes to review a short summary and the key points covered in the tutorial. You might also want to consider cleaning up by dropping any objects you created in the tutorial. For example, you might want to drop the table you created and loaded:

DROP TABLE calendar;

As long as they are no longer needed, you can also drop the other objects you created, such as the storage integration, stage, database, and schema. For details, see Data Definition Language (DDL) commands.