Skip to main content
DatabricksPySparkSnowflake

Must-Know Snowflake Interview Question & Answer(Explained Through Real-World Stories) - Part 3

21. What is Snowflake’s architecture for handling data encryption?​

Story-Driven Explanation​

Imagine you’re shipping a valuable package across the globe. Snowflake ensures that the package is protected both during transit and while it’s stored in the warehouse, using state-of-the-art locks and encryption to keep it safe at every stage of the journey.

Professional / Hands-On Explanation​

Snowflake’s architecture for data encryption is robust and ensures that data is secure both at rest and in transit:

  • Encryption at Rest: All data stored in Snowflake is encrypted using AES-256 encryption. This applies to both structured and semi-structured data.
  • Encryption in Transit: Data transmitted between clients, Snowflake, and external sources is encrypted using TLS (Transport Layer Security), ensuring data is safe during transmission.
  • Key Management: Snowflake handles key management using its automatic encryption key rotation. Additionally, customers can manage their encryption keys through external key management services (e.g., AWS KMS, Azure Key Vault).

22. What is data masking, and how does it work in Snowflake?​

Story-Driven​

Imagine a museum exhibit with valuable artifacts. Instead of putting the original artifacts on display, data masking allows you to show replicas of sensitive items to the public, protecting the originals while allowing people to view the data they need.

Professional / Hands-On​

Data masking in Snowflake is a security feature that allows you to mask sensitive data based on user roles and access levels. This is done using the dynamic data masking feature, where sensitive data (e.g., credit card numbers, social security numbers) is automatically obfuscated when accessed by unauthorized users.

  • Example: You can mask credit card numbers so that users without the proper role see only the last four digits, while authorized users can view the full number.
-- Creating a masked column in Snowflake
CREATE TABLE employees (
    id INT,
    name STRING,
    credit_card_number STRING MASKING POLICY 
    (USING (CASE WHEN CURRENT_ROLE() = 'FULL_ACCESS_ROLE' THEN credit_card_number ELSE 'XXX-XXXX-XXXX' END))
);

23. How do you implement role-based access control (RBAC) in Snowflake?​

Story-Driven​

Imagine a company where different employees have access to different rooms depending on their roles—like the finance team accessing financial data, while the marketing team only has access to campaign performance data. Role-based access control (RBAC) works the same way by assigning specific roles with permissions to access different data or operations in Snowflake.

Professional / Hands-On​

RBAC in Snowflake is implemented by creating roles and granting them appropriate privileges. You can assign roles to users, and these roles determine what actions they can perform (e.g., SELECT, INSERT, UPDATE) and what objects they can access (e.g., databases, tables, schemas).

  • Steps to implement RBAC:

    1. Create roles based on job responsibilities (e.g., DATA_ANALYST, DATA_ENGINEER).
    2. Grant privileges on objects (e.g., tables, views) to those roles.
    3. Assign roles to users.
-- Creating a role and granting permissions
CREATE ROLE data_analyst;
GRANT SELECT ON TABLE sales_data TO ROLE data_analyst;

24. What is materialized view refresh in Snowflake, and how does it impact performance?​

Story-Driven​

Think of a materialized view like a pre-cooked meal in your fridge. Whenever you need to serve it, instead of starting from scratch, you just refresh it to ensure it’s up to date. Materialized view refresh ensures your "meal" (query result) is always fresh without the long wait.

Professional / Hands-On​

In Snowflake, a materialized view is a precomputed view that stores query results for faster access. Materialized view refresh happens automatically in the background or can be triggered manually. When refreshed, the query result is updated, ensuring it reflects the latest data changes.

  • Impact on Performance:

    • Materialized views speed up query performance by storing results of complex queries, but they require periodic refreshes.
    • Frequent refreshes can lead to performance overhead, so it's important to balance refresh rates with query performance needs.
-- Example of refreshing a materialized view
ALTER MATERIALIZED VIEW my_mv REFRESH;

25. How do you manage concurrent users in Snowflake to ensure good performance?​

Story-Driven​

Imagine you’re hosting a large event and you need enough servers to handle all the guests. With Snowflake, you can scale up your servers (virtual warehouses) as the crowd grows and scale them down when they leave, ensuring everyone is served quickly without overloading the system.

Professional / Hands-On​

Snowflake’s architecture allows you to manage concurrent users by using multi-cluster virtual warehouses. A multi-cluster warehouse automatically scales to handle higher concurrency by adding more compute clusters as needed.

  • Auto-scaling: Snowflake automatically adds clusters when the current one reaches a performance threshold and scales down when demand decreases.
  • Warehouse Size: Choose the appropriate virtual warehouse size (e.g., X-Small, Small, Medium) to balance performance and cost.
  • Concurrency Scaling: Snowflake automatically handles spikes in user activity by adding additional clusters during peak demand.
-- Setting up a multi-cluster warehouse
CREATE WAREHOUSE my_warehouse 
  WITH WAREHOUSE_SIZE = 'LARGE' 
  MAX_CLUSTER_COUNT = 5;

26. What is Snowflake’s fail-safe feature, and how does it ensure data reliability?​

Story-Driven​

Think of fail-safe as a backup parachute that ensures safety in case something goes wrong. Even if a system failure happens, Snowflake’s fail-safe guarantees that your data is preserved and recoverable.

Professional / Hands-On​

Fail-safe is a data protection feature in Snowflake that ensures your data is recoverable in the event of a system failure or accidental data loss. It provides an additional 7-day recovery window after the time travel period expires.

  • Note: Fail-safe is not a replacement for backup strategies but serves as an extra safety layer.

27. Explain how query performance is optimized in Snowflake using query profiling.​

Story-Driven​

Imagine you’re trying to run a race, but there are obstacles in your path. Query profiling in Snowflake helps identify these obstacles and removes them, allowing you to run the race faster.

Professional / Hands-On​

Query profiling helps identify performance bottlenecks in SQL queries. Snowflake provides query history and query profiling tools that allow you to analyze the execution plan and time spent in different stages (e.g., scanning, parsing, execution).

  • Use the QUERY_HISTORY function to analyze query performance:

    SELECT * FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY()) 
    WHERE QUERY_TEXT LIKE '%JOIN%' 
    ORDER BY START_TIME DESC;

  • Optimize queries by focusing on long-running stages like large table scans or unnecessary joins.

28. How do you ensure data consistency when using Snowflake data sharing?​

Story-Driven​

Imagine two libraries sharing books. Data consistency ensures that both libraries always have the same version of each book, so readers from both libraries get the same content.

Professional / Hands-On​

When using data sharing in Snowflake, data consistency is maintained by ensuring that any changes made to the shared data are automatically reflected across all consumers, provided the data remains unchanged during the sharing period.

  • Snowflake’s Data Sharing feature ensures that data shared across organizations is consistent and accurate by providing read-only access to shared objects.

29. How does Snowflake handle storage scaling, and what are the key cost considerations?​

Story-Driven​

Imagine you’re running a growing business and need a storage facility that can expand as your inventory grows, without you having to worry about running out of space. Snowflake’s storage scaling lets you do just that, adding storage capacity as needed without manual intervention.

Professional / Hands-On​

Snowflake decouples storage and compute, meaning you can scale storage independently of compute resources. Storage scales automatically as you load more data, and you are only charged for the amount of storage you use.

  • Key cost considerations:

    • Storage cost: You’re billed based on the amount of data stored in Snowflake, including both structured and semi-structured data.
    • Compression: Snowflake uses advanced compression to optimize storage costs.

30. What are external functions in Snowflake, and how do they extend Snowflake’s capabilities?​

Story-Driven​

Think of external functions as calling in an expert for a specific task you can't do within your own warehouse. By integrating external services, you can extend Snowflake’s capabilities to handle specialized tasks like invoking APIs, custom functions, or even running ML models.

Professional​

/ Hands-On External functions allow Snowflake to interact with external services (APIs, Lambda functions, custom services) directly from SQL queries. This extends Snowflake’s functionality by enabling you to process data with external services or integrate with third-party systems.

  • You can invoke an AWS Lambda function or REST API from a Snowflake query.
-- Example of calling an external function from a Snowflake SQL query
SELECT EXTERNAL_FUNCTION('my_lambda_function', 'param1', 'param2');
Career
All Tutorials
Interview Q & A
quizzes