Loading from S3, Azure Blob, GCS in Snowflake - Mini Project: Build & Apply

Loading Data from S3, Azure Blob, and GCS into Snowflake

📖 Scenario: You work as a data engineer. Your task is to load data files stored in cloud storage services into Snowflake tables. The files are stored in Amazon S3, Azure Blob Storage, and Google Cloud Storage (GCS). You will create external stages for each cloud storage, configure access, and load data into Snowflake tables.

🎯 Goal: Build Snowflake external stages for S3, Azure Blob, and GCS with proper credentials and load data from these stages into Snowflake tables.

📋 What You'll Learn

Create an external stage for Amazon S3 with the given bucket and credentials

Create an external stage for Azure Blob Storage with the given container and credentials

Create an external stage for Google Cloud Storage with the given bucket and credentials

Load data from each external stage into a Snowflake table

💡 Why This Matters

🌍 Real World

Data engineers often need to load data from various cloud storage services into Snowflake for analytics and reporting.

💼 Career

Knowing how to configure external stages and load data securely is essential for cloud data platform roles and data engineering jobs.

1

Create an external stage for Amazon S3

Write a Snowflake SQL command to create an external stage called s3_stage that points to the S3 bucket my-s3-bucket/data/. Use the storage integration named my_s3_integration for authentication.

Snowflake

# Write the CREATE STAGE command for S3 here

Hint

Use CREATE OR REPLACE STAGE with the URL set to the S3 bucket path and specify the STORAGE_INTEGRATION for credentials.

2

Create an external stage for Azure Blob Storage

Write a Snowflake SQL command to create an external stage called azure_stage that points to the Azure Blob container mycontainer/data/ in the storage account mystorageaccount. Use the storage integration named my_azure_integration for authentication.

Snowflake

CREATE OR REPLACE STAGE s3_stage
  URL='s3://my-s3-bucket/data/'
  STORAGE_INTEGRATION = my_s3_integration;

# Write the CREATE STAGE command for Azure Blob Storage here

Hint

Use CREATE OR REPLACE STAGE with the URL set to the Azure Blob container path and specify the STORAGE_INTEGRATION.

3

Create an external stage for Google Cloud Storage (GCS)

Write a Snowflake SQL command to create an external stage called gcs_stage that points to the GCS bucket my-gcs-bucket/data/. Use the storage integration named my_gcs_integration for authentication.

Snowflake

CREATE OR REPLACE STAGE s3_stage
  URL='s3://my-s3-bucket/data/'
  STORAGE_INTEGRATION = my_s3_integration;

CREATE OR REPLACE STAGE azure_stage
  URL='azure://mystorageaccount.blob.core.windows.net/mycontainer/data/'
  STORAGE_INTEGRATION = my_azure_integration;

# Write the CREATE STAGE command for GCS here

Hint

Use CREATE OR REPLACE STAGE with the URL set to the GCS bucket path and specify the STORAGE_INTEGRATION.

4

Load data from all external stages into Snowflake tables

Write Snowflake SQL commands to load data from s3_stage into table sales_s3, from azure_stage into table sales_azure, and from gcs_stage into table sales_gcs. Use the COPY INTO command with file format csv_format.

Snowflake

CREATE OR REPLACE STAGE s3_stage
  URL='s3://my-s3-bucket/data/'
  STORAGE_INTEGRATION = my_s3_integration;

CREATE OR REPLACE STAGE azure_stage
  URL='azure://mystorageaccount.blob.core.windows.net/mycontainer/data/'
  STORAGE_INTEGRATION = my_azure_integration;

CREATE OR REPLACE STAGE gcs_stage
  URL='gcs://my-gcs-bucket/data/'
  STORAGE_INTEGRATION = my_gcs_integration;

# Write COPY INTO commands to load data from each stage into respective tables here

Hint

Use COPY INTO with the table name, FROM the stage name prefixed by @, and specify the FILE_FORMAT.

Practice

1. What is the main purpose of using COPY INTO in Snowflake when loading data from S3, Azure Blob, or GCS?

easy

A. To load data files from cloud storage into Snowflake tables

B. To export data from Snowflake to cloud storage

C. To create a new cloud storage bucket

D. To delete files from cloud storage

Solution

1. Step 1: Understand the role of COPY INTO

   The COPY INTO command is used in Snowflake to load data from external cloud storage into Snowflake tables.

2. Step 2: Differentiate from other operations

   Exporting data, creating buckets, or deleting files are not done by COPY INTO. It specifically loads data into tables.

3. Final Answer:

   To load data files from cloud storage into Snowflake tables -> Option A

4. Quick Check:

   Loading data = COPY INTO [OK]

Hint: COPY INTO loads data from cloud storage to tables [OK]

Common Mistakes:

• Confusing COPY INTO with export commands
• Thinking COPY INTO manages cloud storage buckets
• Assuming COPY INTO deletes files

2. Which of the following is the correct syntax to load data from an S3 bucket into a Snowflake table named my_table?

easy

A. COPY INTO my_table FROM @my_s3_stage FILE_FORMAT = (TYPE = 'CSV');

B. LOAD DATA INTO my_table FROM 's3://mybucket/data.csv';

C. INSERT INTO my_table SELECT * FROM s3://mybucket/data.csv;

D. IMPORT INTO my_table FROM @my_s3_stage FORMAT = CSV;

Solution

1. Step 1: Identify correct Snowflake COPY INTO syntax

   Snowflake uses COPY INTO table_name FROM @stage FILE_FORMAT = (TYPE = 'format') to load data.

2. Step 2: Eliminate incorrect options

   LOAD DATA INTO is not valid Snowflake syntax. Direct INSERT INTO ... FROM s3:// paths are not supported. IMPORT INTO does not exist. The correct syntax is COPY INTO my_table FROM @my_s3_stage FILE_FORMAT = (TYPE = 'CSV');.

3. Final Answer:

   COPY INTO my_table FROM @my_s3_stage FILE_FORMAT = (TYPE = 'CSV'); -> Option A

4. Quick Check:

   COPY INTO + stage + file format = correct syntax [OK]

Hint: COPY INTO + @stage + FILE_FORMAT is the right pattern [OK]

Common Mistakes:

• Using LOAD DATA instead of COPY INTO
• Trying to SELECT directly from S3 path
• Using IMPORT INTO which is invalid

3. Given the following Snowflake command:

COPY INTO sales FROM @azure_blob_stage FILE_FORMAT = (TYPE = 'JSON') ON_ERROR = 'CONTINUE';

What happens if one file in the Azure Blob storage has invalid JSON data?

medium

A. The entire load fails and no data is loaded

B. Snowflake automatically fixes the invalid JSON and loads all data

C. Only the invalid file is skipped, and loading continues for others

D. The command ignores the error and loads all files including invalid data

Solution

1. Step 1: Understand ON_ERROR = 'CONTINUE'

   This option tells Snowflake to skip files or rows with errors and continue loading the rest.

2. Step 2: Apply to invalid JSON file

   The invalid JSON file will be skipped, but other valid files will load successfully.

3. Final Answer:

   Only the invalid file is skipped, and loading continues for others -> Option C

4. Quick Check:

   ON_ERROR = CONTINUE skips errors, loads rest [OK]

Hint: ON_ERROR = CONTINUE skips bad files, loads others [OK]

Common Mistakes:

• Assuming entire load fails on one bad file
• Thinking Snowflake auto-fixes JSON errors
• Believing invalid data is loaded anyway

4. You run this command to load data from Google Cloud Storage:

COPY INTO customers FROM @gcs_stage FILE_FORMAT = (TYPE = 'CSV');

But you get an error saying 'Storage integration not authorized'. What is the most likely cause?

medium

A. The GCS bucket is empty

B. The CSV file format is incorrect

C. The Snowflake table does not exist

D. The storage integration lacks permission to access the GCS bucket

Solution

1. Step 1: Analyze the error message

   'Storage integration not authorized' means Snowflake cannot access the cloud storage due to permission issues.

2. Step 2: Identify cause

   The storage integration must have proper permissions to read from the GCS bucket. Other options do not cause authorization errors.

3. Final Answer:

   The storage integration lacks permission to access the GCS bucket -> Option D

4. Quick Check:

   Authorization error = permission issue [OK]

Hint: Authorization errors usually mean permission problems [OK]

Common Mistakes:

• Blaming file format for authorization errors
• Assuming table existence causes storage errors
• Ignoring permission setup for storage integration

5. You want to load multiple CSV files from an S3 bucket into Snowflake, but only files with the prefix 2024/. Which COPY INTO command correctly filters these files?

hard

A. COPY INTO sales FROM @s3_stage FILE_FORMAT = (TYPE = 'CSV') WHERE filename LIKE '2024/%';

B. COPY INTO sales FROM @s3_stage FILE_FORMAT = (TYPE = 'CSV') PATTERN = '^2024/.*';

C. COPY INTO sales FROM @s3_stage FILE_FORMAT = (TYPE = 'CSV') FILES = ('2024/');

D. COPY INTO sales FROM @s3_stage FILE_FORMAT = (TYPE = 'CSV') PATTERN = '2024/.*';

Solution

1. Step 1: Understand file filtering in COPY INTO

   Snowflake uses the PATTERN parameter with a regular expression to filter files by name or prefix.

2. Step 2: Check regex correctness

   PATTERN = '^2024/.*' matches files starting exactly with '2024/'. PATTERN = '2024/.*' without the ^ may match files where '2024/' appears elsewhere in the path. The other options use invalid parameters like WHERE or FILES.

3. Final Answer:

   COPY INTO sales FROM @s3_stage FILE_FORMAT = (TYPE = 'CSV') PATTERN = '^2024/.*'; -> Option B

4. Quick Check:

   PATTERN with ^ prefix filters files correctly [OK]

Hint: Use PATTERN with ^ prefix to filter file names [OK]

Common Mistakes:

• Omitting ^ in regex causing wrong files to load
• Using WHERE or FILES incorrectly for filtering
• Confusing file prefix with file list