15 Snowflake SQL Interview Questions and Answers

Snowflake SQL has emerged as a leading cloud-based data warehousing solution, offering robust performance, scalability, and ease of use. Its unique architecture separates storage and compute, allowing for efficient data management and query processing. Snowflake’s compatibility with various data formats and seamless integration with other tools make it a preferred choice for organizations looking to leverage their data assets effectively.

This article provides a curated selection of Snowflake SQL interview questions designed to help you demonstrate your proficiency and understanding of this powerful platform. By familiarizing yourself with these questions and their answers, you can confidently showcase your expertise and readiness for roles that require strong data warehousing and SQL skills.

Snowflake SQL Interview Questions and Answers

1. How would you insert multiple rows of data into a table?

To insert multiple rows of data into a table in Snowflake SQL, use the INSERT INTO statement with the VALUES clause, which can include multiple sets of values, each representing a row to be inserted.

Example:

INSERT INTO employees (id, name, position)
VALUES 
    (1, 'John Doe', 'Manager'),
    (2, 'Jane Smith', 'Developer'),
    (3, 'Emily Johnson', 'Analyst');

This example inserts three rows into the employees table.

2. Write a query to join two tables on a common column.

Joining tables in Snowflake SQL combines rows from two or more tables based on a related column. Common join types include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN. Each serves a different purpose based on query requirements.

Example of an INNER JOIN:

SELECT 
    a.column1, 
    a.column2, 
    b.column3
FROM 
    table1 a
INNER JOIN 
    table2 b
ON 
    a.common_column = b.common_column;

This query joins table1 and table2 on common_column, selecting specific columns from each.

3. How do you use aggregate functions like COUNT, SUM, and AVG in a query?

Aggregate functions like COUNT, SUM, and AVG perform calculations on multiple rows and return a single value. They are often used with the GROUP BY clause to group rows with the same values into summary rows.

Example:

SELECT department, 
       COUNT(employee_id) AS employee_count, 
       SUM(salary) AS total_salary, 
       AVG(salary) AS average_salary
FROM employees
GROUP BY department;

This query counts employees, sums salaries, and calculates average salaries per department.

4. Write a query that uses a window function to calculate a running total.

Window functions perform calculations across a set of table rows related to the current row, useful for tasks like calculating running totals.

Example of a running total:

SELECT 
    order_id,
    order_date,
    amount,
    SUM(amount) OVER (ORDER BY order_date) AS running_total
FROM 
    orders;

This query calculates a running total of the amount column, ordered by order_date.

5. How do you use CTEs and subqueries to simplify complex queries?

CTEs (Common Table Expressions) and subqueries simplify complex queries by breaking them into smaller parts. CTEs are temporary result sets referenced within a query, while subqueries are nested queries providing intermediate results.

Example:

WITH Sales_CTE AS (
    SELECT 
        Salesperson,
        SUM(Sales) AS Total_Sales
    FROM 
        Sales_Table
    GROUP BY 
        Salesperson
)
SELECT 
    Salesperson,
    Total_Sales
FROM 
    Sales_CTE
WHERE 
    Total_Sales > 10000;

The CTE Sales_CTE pre-aggregates sales data, simplifying the main query.

6. What methods do you use to handle NULL values in your queries?

Handling NULL values in Snowflake SQL can be done using functions like COALESCE, NVL, and NULLIF, or conditions like IS NULL and IS NOT NULL.

Example:

SELECT 
    COALESCE(column_name, 'default_value') AS column_with_default,
    NVL(column_name, 'default_value') AS column_with_default_nvl,
    CASE 
        WHEN column_name IS NULL THEN 'default_value' 
        ELSE column_name 
    END AS column_with_case
FROM 
    table_name
WHERE 
    column_name IS NOT NULL;

7. How do you securely share data with other Snowflake accounts?

Secure data sharing in Snowflake allows sharing data with other accounts without copying or moving it. This is done through secure shares, which are created by the data provider and consumed by the data consumer.

Example SQL commands:

-- In the provider account
CREATE SHARE my_share;

-- Add objects to the share
ALTER SHARE my_share ADD TABLE my_database.my_schema.my_table;

-- Grant privileges on the objects to the share
GRANT SELECT ON my_database.my_schema.my_table TO SHARE my_share;

-- Provide the share to the consumer account
ALTER SHARE my_share SET ACCOUNTS = ('consumer_account');

-- In the consumer account
CREATE DATABASE my_shared_db FROM SHARE provider_account.my_share;

8. What techniques do you use for performance tuning in Snowflake?

Performance tuning in Snowflake involves optimizing queries, implementing clustering keys, using materialized views, and managing resources efficiently.

Key strategies include:

– Query Optimization: Use the query profiler to analyze and optimize queries.
– Data Clustering: Implement clustering keys to improve query performance.
– Materialized Views: Use them to precompute and store complex query results.
– Resource Management: Allocate appropriate virtual warehouses and use auto-scaling features.

9. How do you efficiently load large datasets into Snowflake?

Efficiently loading large datasets into Snowflake involves using the COPY command, optimizing file formats, leveraging parallel processing, and using staging areas.

Key strategies include:

– Use the COPY Command: Optimized for bulk loading from various sources.
– Optimize File Formats: Use compressed formats like CSV, Parquet, or ORC.
– Leverage Parallel Processing: Load data in parallel to speed up the process.
– Use Staging Areas: Temporarily store data for validation and transformation.

10. Write a query to parse and query JSON data stored in a Snowflake table.

Snowflake SQL supports semi-structured data, including JSON, using the VARIANT data type. The FLATTEN function can transform nested JSON structures into a relational format.

Example:

-- Create a table with a VARIANT column
CREATE OR REPLACE TABLE json_table (
    id INTEGER,
    data VARIANT
);

-- Insert JSON data into the table
INSERT INTO json_table (id, data) VALUES
(1, PARSE_JSON('{"name": "John", "age": 30, "address": {"city": "New York", "zip": "10001"}}')),
(2, PARSE_JSON('{"name": "Jane", "age": 25, "address": {"city": "San Francisco", "zip": "94105"}}'));

-- Query the JSON data
SELECT
    id,
    data:name::STRING AS name,
    data:age::INTEGER AS age,
    data:address.city::STRING AS city,
    data:address.zip::STRING AS zip
FROM json_table;

11. How do you create and use materialized views to improve query performance?

Materialized views in Snowflake enhance query performance by storing query results physically. They are beneficial for frequently executed complex queries.

Example:

CREATE MATERIALIZED VIEW my_materialized_view AS
SELECT column1, column2, SUM(column3) AS total
FROM my_table
GROUP BY column1, column2;

Query the materialized view like a regular table:

SELECT * FROM my_materialized_view
WHERE column1 = 'some_value';

Snowflake automatically refreshes materialized views, but manual refresh is also possible:

ALTER MATERIALIZED VIEW my_materialized_view REFRESH;

12. How does Snowflake handle semi-structured data like JSON and Avro?

Snowflake handles semi-structured data like JSON and Avro using the VARIANT data type, which allows flexible schema design. Built-in functions enable efficient parsing and querying.

Example:

CREATE TABLE json_table (data VARIANT);

INSERT INTO json_table (data) VALUES
    (PARSE_JSON('{"name": "John", "age": 30, "city": "New York"}')),
    (PARSE_JSON('{"name": "Jane", "age": 25, "city": "San Francisco"}'));

SELECT data:name::STRING AS name, data:age::NUMBER AS age, data:city::STRING AS city
FROM json_table;

13. What is Snowflake’s Fail-safe feature and how does it differ from Time Travel?

Snowflake’s Fail-safe and Time Travel are data recovery mechanisms. Time Travel allows access to historical data within a defined period, useful for recovering from accidental modifications. Fail-safe provides an additional seven days of data retention after Time Travel, intended for disaster recovery.

14. How do you monitor and optimize query performance in Snowflake?

Monitoring and optimizing query performance in Snowflake involves using tools like the Query Profile and QUERY_HISTORY view to track performance and identify bottlenecks. Optimization strategies include using clustering keys, result caching, and efficient use of virtual warehouses.

15. Describe the different types of data sharing options available in Snowflake.

Snowflake offers several data sharing options:

– Secure Data Sharing: Share data with other accounts without copying it.
– Reader Accounts: Allow access to shared data for organizations without a Snowflake account.
– Data Marketplace: Publish and share datasets with a broader audience.
– Private Data Exchange: Share data securely within an ecosystem, including partners and customers.