Load data from cloud storage: Amazon S3

Introduction

This tutorial shows you how to load data from an Amazon S3 bucket into Snowflake using SQL. You can access a pre-loaded Snowsight template worksheet to complete these tasks.

Note

Snowflake bills a minimal amount for the on-disk storage used for any sample data in this tutorial. The tutorial provides steps to drop objects and minimize storage cost. Snowflake requires a virtual warehouse to load the data and execute queries. A running virtual warehouse consumes Snowflake credits.

If you are using a 30-day trial account, which provides free credits, you won’t incur any costs.

What you will learn

In this tutorial you will learn how to:

  • Use a role that has the privileges to create and use the Snowflake objects required by this tutorial.
  • Use a warehouse to access resources.
  • Select a database and schema to use for the session.
  • Create a table.
  • Create a storage integration for your cloud platform.
  • Create a stage for your storage integration.
  • Load data into the table from the stage.
  • Query the data in the table.

Prerequisites

This tutorial assumes the following:

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 SQL worksheet for loading data from Amazon S3

You can use worksheets to write and run SQL commands on your database. You can access a pre-loaded 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.

To open the pre-loaded template worksheet, follow these steps:

  1. In the navigation menu, select Projects » Templates.

  2. Find and open Load data from Amazon AWS.

    The beginning of your worksheet looks similar to the following image:

SQL load from cloud template worksheet, which contains the SQL commands for this tutorial, along with descriptive comments.

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. You can use the SNOWFLAKE_LEARNING_WH warehouse 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;

  2. At the top of the worksheet, select Run.

    Note

    In this tutorial, run SQL statements one at a time. Don’t select Run all.

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

    USE WAREHOUSE SNOWFLAKE_LEARNING_WH;

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.

In this tutorial, you use the database SNOWFLAKE_LEARNING_DB, a schema that concatenates your username with _LOAD_SAMPLE_DATA_FROM_S3, and a table that you create named calendar.

To select this database and schema for use in the session and create the table, do the following:

  1. In the open worksheet, place your cursor in the USE DATABASE line, then select Run.

    USE DATABASE SNOWFLAKE_LEARNING_DB;

  2. Place your cursor in each SET line, then select Run.

    SET user_name = current_user();
SET schema_name = CONCAT($user_name, '_LOAD_DATA_FROM_AMAZON_AWS');

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

    USE SCHEMA IDENTIFIER($schema_name);

  4. Place your cursor in the CREATE 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 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 S3 calendar data file';

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

    SELECT * FROM calendar;

    The output shows the columns of the table you created. Currently, the table doesn’t 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 Amazon S3 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 AWS identity and access management (IAM) user ID.

To create a storage integration for Amazon S3, do the following:

  1. Use the AWS Management Console to create an IAM policy and an IAM role. These resources provide secure access to your S3 bucket for loading data. You will need these resources to create a storage integration in Snowflake. After logging into the console, complete Steps 1 and 2 under Option 1: Configure a Snowflake storage integration to access Amazon S3.

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

    CREATE OR REPLACE STORAGE INTEGRATION s3_data_integration
  TYPE = EXTERNAL_STAGE
  STORAGE_PROVIDER = 'S3'
  STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::123456789012:role/tutorial_role'
  ENABLED = TRUE
  STORAGE_ALLOWED_LOCATIONS = ('s3://snow-tutorial-bucket/s3data/');

    Set STORAGE_AWS_ROLE_ARN to the unique identifier for the IAM role that you created previously. You can find this value under IAM > Roles in the AWS Management Console.

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

    DESCRIBE INTEGRATION s3_data_integration;

    This command retrieves the ARN and external ID for the AWS IAM user that was created automatically for your Snowflake account. You will use these values to configure permissions for Snowflake in the AWS Management Console.

    The output for this command looks similar to the following:

    Output of DESCRIBE INTEGRATION command, with the following columns: property, property_type, property_value, property_default.

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

    SHOW INTEGRATIONS;

    The output for this command looks similar to the following:

    Output of SHOW INTEGRATIONS command, with the following columns: name, type, category, enabled, comment, created_on.

  5. Use the AWS Management Console to configure permissions for the IAM user to access storage buckets. Follow Step 5 under Option 1: Configure a Snowflake storage integration to access Amazon S3.

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 S3 bucket.

To create a stage, do the following:

  1. In the open worksheet, place your cursor in the CREATE 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.s3_data.s3data_stage
  STORAGE_INTEGRATION = s3_data_integration
  URL = 's3://snow-tutorial-bucket/s3data/'
  FILE_FORMAT = (TYPE = CSV);

  2. Return information about the stage you created:

    SHOW STAGES;

    The output for this command looks similar to the following:

    Output of SHOW STAGES command, with the following columns: created_on, name, database_name, schema_name, url.

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 S3 buckets, see Copying data from an S3 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.s3_data.calendar
  FROM @cloud_data_db.s3_data.s3data_stage
    FILES = ('calendar.txt');

Your output looks similar to the following image:

Five rows are copied into the table. The output has the following columns: file, status, rows_parsed, rows_loaded, error_limit.

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 calendar;

Your output looks similar to the following image:

All the rows in the table are selected. This example has full_date, day_name, month_name, day_num, year_num, and holiday columns.

Step 9. Cleanup, summary, and additional resources

Congratulations! You have successfully completed this tutorial.

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 and stage. For details, see Data Definition Language (DDL) commands.

Summary and key points

In summary, you used a pre-loaded template worksheet in Snowsight to complete the following steps:

  1. Set the role and warehouse to use.
  2. Select a database and schema to use for the session.
  3. Create a table.
  4. Create a storage integration and configure permissions on cloud storage.
  5. Create a stage and load the data from the stage into the table.
  6. Query the data.

Here are some key points to remember about loading and querying data:

  • You need the required permissions to create and manage objects in your account. In this tutorial, you use the ACCOUNTADMIN system role for these privileges.

    This role is not normally used to create objects. Instead, we recommend creating a hierarchy of roles aligned with business functions in your organization. For more information, see Using the ACCOUNTADMIN Role.

  • You need a warehouse for the resources required to create and manage objects and run SQL commands. This tutorial uses the SNOWFLAKE_LEARNING_WH warehouse included with the template environment.

  • You used a database to store the data and a schema to group the database objects logically.

  • You created a storage integration and a stage to load data from a CSV file stored in an Amazon S3 bucket.

  • After the data was loaded into your database, you queried it using SELECT statements.

What’s next?

Continue learning about Snowflake using the following resources:

Next
Prerequisites