Loading from S3, Azure Blob, GCS in Snowflake - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a stage for loading data from an S3 bucket.

Snowflake

CREATE OR REPLACE STAGE my_s3_stage URL='s3://mybucket/data/' [1] = (AWS_KEY_ID='your_key' AWS_SECRET_KEY='your_secret');

Drag options to blanks, or click blank then click option'

AENCRYPTION

BSTORAGE_INTEGRATION

CFILE_FORMAT

DCREDENTIALS

Attempts:

3 left

💡 Hint

Common Mistakes

Using FILE_FORMAT instead of CREDENTIALS for AWS keys.

Using ENCRYPTION keyword incorrectly here.

The CREDENTIALS keyword is used to provide AWS access keys for the S3 stage.

2fill in blank

medium

Complete the code to load data from an Azure Blob storage stage into a Snowflake table.

Snowflake

COPY INTO my_table FROM @my_azure_stage/[1] FILE_FORMAT = (TYPE = 'CSV' FIELD_DELIMITER = ',');

Drag options to blanks, or click blank then click option'

Adata.csv

Bdata.parquet

Cfolder/

Ddata.json

Attempts:

3 left

💡 Hint

Common Mistakes

Using a JSON or Parquet file name when the format is CSV.

Using a folder path instead of a file name.

The file data.csv matches the CSV file format specified in the COPY command.

3fill in blank

hard

Fix the error in the Snowflake stage creation for Google Cloud Storage by completing the missing keyword.

Snowflake

CREATE OR REPLACE STAGE gcs_stage URL='gcs://mybucket/data/' [1] = (GCS_ACCESS_TOKEN='your_token');

Drag options to blanks, or click blank then click option'

AFILE_FORMAT

BENCRYPTION

CCREDENTIALS

DSTORAGE_INTEGRATION

Attempts:

3 left

💡 Hint

Common Mistakes

Using ENCRYPTION or STORAGE_INTEGRATION instead of CREDENTIALS.

Omitting the keyword entirely.

The CREDENTIALS keyword is required to provide the GCS access token for authentication.

4fill in blank

hard

Fill both blanks to create a file format for loading JSON files from cloud storage.

Snowflake

CREATE OR REPLACE FILE FORMAT my_json_format TYPE = '[1]' STRIP_OUTER_ARRAY = [2];

Drag options to blanks, or click blank then click option'

AJSON

BTRUE

CFALSE

DCSV

Attempts:

3 left

💡 Hint

Common Mistakes

Using CSV as the file format type for JSON files.

Setting STRIP_OUTER_ARRAY to FALSE when JSON arrays need to be stripped.

The file format type for JSON files is JSON, and STRIP_OUTER_ARRAY = TRUE helps parse JSON arrays correctly.

5fill in blank

hard

Fill all three blanks to copy data from an S3 stage into a table with a specific file format and error handling.

Snowflake

COPY INTO my_table FROM @my_s3_stage/[1] FILE_FORMAT = (TYPE = '[2]') ON_ERROR = '[3]';

Drag options to blanks, or click blank then click option'

Adata.csv

BCSV

CCONTINUE

Ddata.json

Attempts:

3 left

💡 Hint

Common Mistakes

Using a JSON file name with CSV file format.

Using ON_ERROR = 'ABORT_STATEMENT' instead of CONTINUE when skipping errors is desired.

The file data.csv matches the CSV file format, and ON_ERROR = 'CONTINUE' tells Snowflake to skip errors and continue loading.

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