SnowPro Associate: Platform Question 186
Single answer3.1 Describe considerations when working with structured and semi-structured data.A retail company loads daily clickstream JSON files from cloud storage into a Snowflake table named RAW_EVENTS with a single VARIANT column called EVENT_DATA. Analysts need to report on the customer ID, event timestamp, and product ID. They also discovered that some JSON documents contain arrays of products, while others contain only one product object. The BI team wants a solution that minimizes data loss from schema changes in the source files while still allowing efficient SQL analysis. Which approach should the data engineer recommend?
- A
Load the JSON into separate relational columns during ingestion, rejecting any records that contain unexpected attributes so the schema remains fixed for reporting.
- B
Store the raw JSON in the VARIANT column, then use SQL to extract needed attributes and use FLATTEN when arrays must be expanded into rows.
- C
Convert the JSON files to CSV before loading so that Snowflake can automatically normalize arrays and nested objects into separate columns.
- D
Load the JSON into an external table with VARCHAR columns for all fields, because semi-structured data cannot be queried efficiently from VARIANT.
Show answer and explanation
Correct answer: B
Explanation
The best recommendation is to store the raw JSON in a VARIANT column and extract fields as needed using SQL. This is a core Snowflake pattern for semi-structured data because it preserves source fidelity, accommodates schema drift, and enables direct querying of nested attributes. When JSON contains arrays, Snowflake's FLATTEN function can transform those arrays into rows for reporting. This approach is commonly preferred over prematurely enforcing a rigid relational schema, especially when source documents may evolve. Relevant Snowflake documentation includes guidance on querying semi-structured data, using VARIANT to store JSON, and using the FLATTEN table function to work with arrays and nested structures.
- A. Incorrect.
Incorrect. For semi-structured data such as JSON, forcing all attributes into fixed relational columns at ingestion can cause unnecessary rigidity and potential data loss when the source schema evolves. Rejecting records with unexpected attributes contradicts a common Snowflake best practice of preserving raw semi-structured data first, especially when source formats may change.
- B. Correct.
Correct. Storing JSON in a VARIANT column preserves the full payload and reduces the risk of losing new or changing attributes. Snowflake supports querying nested elements directly with SQL path notation, and the FLATTEN table function is the standard way to explode arrays into rows for analysis. This approach supports both flexibility and practical querying requirements.
- C. Incorrect.
Incorrect. Converting JSON to CSV before loading is not a best practice for nested semi-structured data. CSV does not naturally preserve nested objects and arrays without custom transformation, which can introduce complexity and data quality issues. Snowflake is designed to ingest and query JSON directly through VARIANT.
- D. Incorrect.
Incorrect. External tables can be used with semi-structured data, but the statement that semi-structured data cannot be queried efficiently from VARIANT is false. Snowflake provides native support for querying VARIANT data, including nested fields and arrays. Storing all fields as VARCHAR would also remove native typing benefits and make downstream parsing more cumbersome.