SnowPro Associate: Platform Question 187
Single answer3.1 Describe considerations when working with structured and semi-structured data.A retail company stores raw clickstream events in a Snowflake table with a VARIANT column named EVENT_DATA. Analysts frequently query fields such as EVENT_DATA:customer.id, EVENT_DATA:device.os, and EVENT_DATA:order.total. Query performance is degrading as data volume grows, and the team wants to improve performance without losing the flexibility to retain the original semi-structured payload. Which approach is the MOST appropriate?
- A
Create a relational table with typed columns for frequently accessed attributes extracted from EVENT_DATA, while continuing to store the original payload in VARIANT for less common fields.
- B
Convert the VARIANT column to a VARCHAR column so Snowflake can scan the JSON text more efficiently during filtering and aggregation.
- C
Replace the VARIANT column with an ARRAY column because ARRAY is optimized for nested key-value data and supports faster path traversal.
- D
Store each JSON attribute in a separate row in a normalized key-value table and remove the original semi-structured column to reduce storage overhead.
Show answer and explanation
Correct answer: A
Explanation
When working with structured and semi-structured data in Snowflake, an important consideration is balancing flexibility with performance and ease of analysis. VARIANT is well suited for ingesting raw JSON and preserving evolving schemas, but repeatedly querying deeply nested paths at scale can be less convenient than using structured, typed columns for common access patterns. A practical design is to keep the original JSON in VARIANT and also project frequently used elements into relational columns. This supports both schema-on-read flexibility and efficient analytics. Snowflake documentation on semi-structured data explains that VARIANT stores hierarchical data such as JSON, while standard relational columns remain preferable for stable, frequently queried attributes.
- A. Correct.
Correct. This is a common best-practice pattern in Snowflake: retain the raw semi-structured data in a VARIANT column for flexibility and auditing, while extracting high-value, frequently queried attributes into structured, typed columns. This improves usability and can improve query performance because analysts can filter, join, and aggregate on native columnar data types instead of repeatedly traversing semi-structured paths.
- B. Incorrect.
Incorrect. Converting JSON data from VARIANT to VARCHAR generally makes querying worse, not better. Snowflake's semi-structured support is designed around VARIANT, which preserves the structure and allows path-based access. Storing raw JSON as text removes type awareness and makes filtering and aggregation less efficient and less reliable.
- C. Incorrect.
Incorrect. ARRAY is intended for ordered lists of values, not as a replacement for object-style JSON with named attributes. JSON documents with key-value pairs are represented in Snowflake using OBJECT within VARIANT. Replacing VARIANT with ARRAY would not be an appropriate optimization for this scenario.
- D. Incorrect.
Incorrect. Although a key-value design may seem flexible, breaking each attribute into separate rows usually increases complexity and makes analytics, filtering, and reconstruction of the original event more difficult. It also removes the benefit of preserving the raw payload in its native semi-structured form.