File formats (CSV, JSON, Parquet, Avro) in Snowflake - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a CSV file into a Snowflake table.

Snowflake

COPY INTO my_table FROM @my_stage/file[1] FILE_FORMAT = (TYPE = 'CSV');

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

A.csv

B.json

C.parquet

D.avro

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong file extension like .json or .parquet for CSV files.

The .csv extension is used for CSV files, which are comma-separated values files commonly used for tabular data.

2fill in blank

medium

Complete the code to specify the JSON file format in Snowflake.

Snowflake

CREATE FILE FORMAT my_json_format TYPE = '[1]';

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

ACSV

BPARQUET

CJSON

DAVRO

Attempts:

3 left

💡 Hint

Common Mistakes

Using CSV or Parquet instead of JSON for JSON files.

The file format type JSON is used to specify JSON files in Snowflake.

3fill in blank

hard

Fix the error in the COPY INTO command to load Parquet files.

Snowflake

COPY INTO my_table FROM @my_stage/file[1] FILE_FORMAT = (TYPE = 'PARQUET');

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

A.parquet

B.json

C.avro

D.csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using .csv or .json extensions when loading Parquet files.

Parquet files use the .parquet extension, so the file path must end with this extension to load correctly.

4fill in blank

hard

Fill both blanks to create a file format for Avro files with compression.

Snowflake

CREATE FILE FORMAT avro_format TYPE = '[1]' COMPRESSION = '[2]';

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

AAVRO

BGZIP

CSNAPPY

DJSON

Attempts:

3 left

💡 Hint

Common Mistakes

Using JSON as file type for Avro or wrong compression types.

The file format type for Avro files is AVRO. A common compression used with Avro is SNAPPY.

5fill in blank

hard

Fill all three blanks to create a Snowflake stage for Parquet files with auto compression detection.

Snowflake

CREATE STAGE my_stage FILE_FORMAT = (TYPE = '[1]' COMPRESSION = '[2]' AUTO_DETECT = [3]);

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

ACSV

BPARQUET

CTRUE

DAUTO

Attempts:

3 left

💡 Hint

Common Mistakes

Using CSV as the file type or setting an incorrect compression value for Parquet files.

Parquet files use PARQUET as the type. Setting COMPRESSION to AUTO enables Snowflake to detect the compression automatically because Parquet files are often already compressed. AUTO_DETECT set to TRUE lets Snowflake detect file format details automatically.

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