Snoflake Production Guide

Comprehensive guide covering essential Snowflake concepts, features, and common interview topics for data engineering, data analyst, and architect positions.

1. Core Architecture Components

Key Concepts to Master:

2. Performance Optimization Techniques

Query Performance:

Common Performance Interview Questions:

  1. How would you optimize a slow-running query?
  2. When would you use clustering keys vs partitioning?
  3. Explain the difference between scaling up vs scaling out
  4. How does result caching work and when does it apply?

3. Data Loading and Unloading

Loading Methods:

File Formats and Stages:

4. Security Features

Access Control:

Data Protection:

5. Time Travel and Data Protection

Time Travel:

Fail-safe:

6. Cost Optimization Strategies

Compute Cost Management:

Storage Cost Management:

7. Advanced Features

Data Sharing:

Semi-structured Data:

Programmability:

8. Common Interview Scenarios

Scenario-based Questions:

  1. High Concurrency Issue:
  2. Large Data Migration:
  3. Real-time Analytics:
  4. Cost Spike Investigation:

9. SQL and Query Patterns

Important SQL Features:

10. Monitoring and Troubleshooting

Key Views and Functions:

11. Best Practices to Mention

Development Best Practices:

  1. Use appropriate warehouse sizes - Don't over-provision
  2. Implement proper role hierarchy - Security first approach
  3. Design efficient data models - Consider denormalization
  4. Use clustering keys wisely - Only for large tables with specific access patterns
  5. Monitor and set resource monitors - Prevent runaway costs
  6. Leverage caching - Understand three-layer caching
  7. Use COPY instead of INSERT - For bulk operations
  8. Partition large files - 100-250MB compressed ideal
  9. Clean up temporary objects - Reduce storage costs
  10. Document data lineage - Use tags and comments

12. Recent Features to Know

Latest Additions (2024-2025):

13. Sample Technical Questions

Architecture:

  1. Explain how Snowflake's architecture differs from traditional data warehouses
  2. What happens when you submit a query in Snowflake?
  3. How does Snowflake handle concurrency?

Performance:

  1. How would you improve the performance of a slow query?
  2. When would you use a larger warehouse vs. a multi-cluster warehouse?
  3. Explain the different caching layers in Snowflake

Data Loading:

  1. Compare COPY command vs Snowpipe for data ingestion
  2. How would you handle semi-structured data in Snowflake?
  3. Design a solution for real-time data ingestion

Security:

  1. How would you implement row-level security?
  2. Explain the role hierarchy in Snowflake
  3. How does Snowflake encrypt data?

14. Hands-on Skills to Demonstrate

© Kevin Thomas Luzbetak, MSc