Semi-structured data like JSON is common and flexible. Handling it lets you extract useful info from messy or nested data.
0
0
Semi-structured data handling (JSON) in dbt
Introduction
You get data from web APIs that return JSON responses.
You store user preferences or settings in JSON format.
You analyze logs or events saved as JSON strings.
You want to flatten nested JSON data for easier querying.
You combine structured tables with JSON columns in your database.
Syntax
dbt
select json_extract_path_text(json_column, 'key') as value from table_name
Use json_extract_path_text to get text values from JSON keys.
dbt runs SQL on your warehouse, so JSON functions depend on your database (e.g., Snowflake, Redshift).
Examples
Extracts the 'name' field from the JSON column
user_data.dbt
select json_extract_path_text(user_data, 'name') as user_name from users
Extracts nested key 'type' inside 'event' from JSON column
event_json.dbt
select json_extract_path_text(event_json, 'event', 'type') as event_type from events
Gets the first element from a JSON array stored in
json_array.dbt
select json_extract_array_element_text(json_array, 0) as first_item from data_table
Sample Program
This example shows how to extract a nested user name and the second action from JSON data stored in json_col.
dbt
with raw_data as ( select '{"user": {"id": "123", "name": "Alice"}, "actions": ["login", "purchase"]}' as json_col ) select json_extract_path_text(json_col, 'user', 'name') as user_name, json_extract_array_element_text(json_extract_path_text(json_col, 'actions'), 1) as second_action from raw_data
OutputSuccess
Important Notes
JSON functions vary by database; check your warehouse docs for exact syntax.
Extracted JSON values are usually text; convert types if needed.
Use CTEs in dbt models to organize JSON extraction steps clearly.
Summary
Semi-structured JSON data is common and flexible.
Use JSON extraction functions to get nested info in dbt SQL models.
Check your database's JSON function support for best results.