File formats (CSV, JSON, Parquet, Avro) in Snowflake - Mini Project: Build & Apply

Working with File Formats in Snowflake

📖 Scenario: You are a data engineer working with Snowflake. You need to prepare and load data files in different formats like CSV, JSON, Parquet, and Avro into Snowflake tables.

🎯 Goal: Build Snowflake SQL commands step-by-step to create tables and stage files in CSV, JSON, Parquet, and Avro formats, and configure file format objects for loading data correctly.

📋 What You'll Learn

Create a table for customer data

Create a file format for CSV files

Create a file format for JSON files

Create a file format for Parquet files

Create a file format for Avro files

💡 Why This Matters

🌍 Real World

Data engineers often need to prepare Snowflake tables and file formats to load data from various file types efficiently.

💼 Career

Understanding how to configure file formats and stages in Snowflake is essential for roles involving data ingestion and cloud data warehousing.

1

Create a table for customer data

Write a Snowflake SQL command to create a table called customers with columns id as INTEGER, name as VARCHAR(100), and email as VARCHAR(100).

Snowflake

-- Your code here

Hint

Use CREATE OR REPLACE TABLE customers and define the columns with their data types.

2

Create a file format for CSV files

Write a Snowflake SQL command to create a file format called csv_format for CSV files with FIELD_DELIMITER as comma and SKIP_HEADER set to 1.

Snowflake

CREATE OR REPLACE TABLE customers (
    id INTEGER,
    name VARCHAR(100),
    email VARCHAR(100)
);
-- Your code here

Hint

Use CREATE OR REPLACE FILE FORMAT csv_format and specify CSV type with the correct options.

3

Create file formats for JSON, Parquet, and Avro files

Write Snowflake SQL commands to create file formats called json_format, parquet_format, and avro_format for JSON, Parquet, and Avro file types respectively.

Snowflake

CREATE OR REPLACE TABLE customers (
    id INTEGER,
    name VARCHAR(100),
    email VARCHAR(100)
);

CREATE OR REPLACE FILE FORMAT csv_format
    TYPE = 'CSV'
    FIELD_DELIMITER = ','
    SKIP_HEADER = 1;
-- Your code here

Hint

Use CREATE OR REPLACE FILE FORMAT for each format with the correct TYPE.

4

Complete by creating a stage for loading files

Write a Snowflake SQL command to create a stage called customer_stage that uses the csv_format file format.

Snowflake

CREATE OR REPLACE TABLE customers (
    id INTEGER,
    name VARCHAR(100),
    email VARCHAR(100)
);

CREATE OR REPLACE FILE FORMAT csv_format
    TYPE = 'CSV'
    FIELD_DELIMITER = ','
    SKIP_HEADER = 1;

CREATE OR REPLACE FILE FORMAT json_format
    TYPE = 'JSON';

CREATE OR REPLACE FILE FORMAT parquet_format
    TYPE = 'PARQUET';

CREATE OR REPLACE FILE FORMAT avro_format
    TYPE = 'AVRO';
-- Your code here

Hint

Use CREATE OR REPLACE STAGE customer_stage and specify the FILE_FORMAT option.

Practice

1. Which file format in Snowflake is best suited for storing hierarchical data with nested structures?

easy

A. Avro

B. JSON

C. Parquet

D. CSV

Solution

1. Step 1: Understand file format characteristics

   JSON supports nested and hierarchical data structures naturally, unlike CSV which is flat.

2. Step 2: Compare JSON with other formats

   Parquet and Avro also support nested data but JSON is most commonly used for hierarchical data due to its readability and flexibility.

3. Final Answer:

   JSON -> Option B

4. Quick Check:

   Hierarchical data = JSON [OK]

Hint: Nested data? Think JSON first [OK]

Common Mistakes:

• Choosing CSV for nested data
• Confusing Parquet with JSON for readability
• Assuming Avro is always best for nested data

2. Which Snowflake file format option correctly specifies that the CSV file uses a semicolon as the field delimiter?

easy

A. FIELD_DELIMITER = ';'

B. FIELD_DELIMITER = ','

C. FIELD_DELIMITER = ':'

D. FIELD_DELIMITER = '|'

Solution

1. Step 1: Identify the delimiter option for CSV in Snowflake

   Snowflake uses FIELD_DELIMITER to specify the character separating fields in CSV files.

2. Step 2: Match the semicolon delimiter

   The semicolon character is ';', so FIELD_DELIMITER = ';' is correct.

3. Final Answer:

   FIELD_DELIMITER = ';' -> Option A

4. Quick Check:

   Semicolon delimiter = FIELD_DELIMITER ';' [OK]

Hint: Delimiter option is FIELD_DELIMITER [OK]

Common Mistakes:

• Using comma instead of semicolon
• Confusing FIELD_DELIMITER with RECORD_DELIMITER
• Using wrong delimiter characters

3. Given this Snowflake file format definition for JSON:

CREATE FILE FORMAT my_json_format TYPE = 'JSON' STRIP_OUTER_ARRAY = TRUE;

What happens when you load a JSON file containing an outer array of objects?

medium

A. Snowflake loads the entire array as a single row

B. Snowflake ignores the outer array and loads nothing

C. Snowflake throws an error due to the outer array

D. Snowflake loads each object inside the outer array as a separate row

Solution

1. Step 1: Understand STRIP_OUTER_ARRAY option

   This option tells Snowflake to treat each element inside the outer JSON array as a separate record.

2. Step 2: Apply to loading behavior

   When loading, Snowflake will parse the outer array and load each object inside it as its own row.

3. Final Answer:

   Snowflake loads each object inside the outer array as a separate row -> Option D

4. Quick Check:

   STRIP_OUTER_ARRAY TRUE = separate rows [OK]

Hint: STRIP_OUTER_ARRAY TRUE splits array into rows [OK]

Common Mistakes:

• Thinking entire array loads as one row
• Expecting an error on outer array
• Assuming outer array is ignored

4. You created a Snowflake file format for CSV with:

CREATE FILE FORMAT my_csv_format TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"';

When loading data, some fields with commas inside quotes are split incorrectly. What is the likely issue?

medium

A. FIELD_DELIMITER is missing and defaults to tab

B. FIELD_OPTIONALLY_ENCLOSED_BY should be set to single quote instead of double quote

C. The CSV file uses a different enclosing character than specified

D. The file format type should be JSON, not CSV

Solution

1. Step 1: Check FIELD_OPTIONALLY_ENCLOSED_BY usage

   This option tells Snowflake which character encloses fields optionally, often double quotes for CSV.

2. Step 2: Identify mismatch with actual file

   If the CSV file uses a different enclosing character (like single quotes), Snowflake will not parse fields with commas correctly.

3. Final Answer:

   The CSV file uses a different enclosing character than specified -> Option C

4. Quick Check:

   Enclosing char mismatch breaks parsing [OK]

Hint: Match enclosing char exactly to file [OK]

Common Mistakes:

• Changing enclosing char without checking file
• Assuming FIELD_DELIMITER defaults to comma always
• Switching file format type unnecessarily

5. You want to load a large dataset with complex nested data and efficient compression into Snowflake. Which file format should you choose and why?

hard

A. Parquet, because it supports nested data and is optimized for compression and performance

B. JSON, because it supports nested data and is human-readable

C. CSV, because it is simple and widely supported

D. Avro, because it only supports flat data but is fast

Solution

1. Step 1: Identify requirements

   The dataset is large, has nested data, and needs efficient compression and performance.

2. Step 2: Compare file formats

   CSV is flat and not compressed; JSON is nested but less efficient; Avro supports nested but less optimized than Parquet; Parquet supports nested data and is columnar, offering better compression and query speed.

3. Final Answer:

   Parquet, because it supports nested data and is optimized for compression and performance -> Option A

4. Quick Check:

   Large nested data + compression = Parquet [OK]

Hint: Large nested data? Pick Parquet for speed and size [OK]

Common Mistakes:

• Choosing CSV for nested data
• Preferring JSON despite compression needs
• Misunderstanding Avro's capabilities