Semi-structured data handling (JSON) in dbt

Introduction

Semi-structured data like JSON is common and flexible. Handling it lets you extract useful info from messy or nested data.

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

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.

Practice

1. What is the main purpose of using JSON functions in dbt when working with semi-structured data?

easy

A. To convert JSON data into CSV files automatically

B. To encrypt JSON data for security

C. To create new JSON files from scratch

D. To extract and manipulate nested data inside JSON columns

Solution

1. Step 1: Understand JSON data in dbt

   JSON data often contains nested information that needs to be accessed or transformed.

2. Step 2: Purpose of JSON functions

   JSON functions allow extracting specific parts of this nested data for analysis or transformation.

3. Final Answer:

   To extract and manipulate nested data inside JSON columns -> Option D

4. Quick Check:

   JSON functions = extract nested data [OK]

Hint: JSON functions help get nested info from JSON columns [OK]

Common Mistakes:

• Thinking JSON functions create files
• Assuming JSON functions encrypt data
• Confusing JSON extraction with file conversion

2. Which of the following is the correct syntax to extract a value from a JSON column named data using dbt's SQL with PostgreSQL syntax?

easy

A. SELECT data->name AS name FROM table_name;

B. SELECT data->>'name' AS name FROM table_name;

C. SELECT data['name'] AS name FROM table_name;

D. SELECT data->>name AS name FROM table_name;

Solution

1. Step 1: Identify PostgreSQL JSON extraction syntax

   PostgreSQL uses ->> to extract JSON text values by key as text.

2. Step 2: Apply correct syntax to extract 'name'

   The correct syntax is data->>'name' to get the text value of the 'name' key.

3. Final Answer:

   SELECT data->>'name' AS name FROM table_name; -> Option B

4. Quick Check:

   PostgreSQL JSON text extraction = data->>'key' [OK]

Hint: Use ->> with quotes for JSON text extraction in PostgreSQL [OK]

Common Mistakes:

• Using square brackets instead of ->>
• Missing quotes around the key name
• Using -> instead of ->> for text extraction

3. Given the following dbt SQL snippet using Snowflake syntax:

SELECT
  data:id::string AS user_id,
  data:attributes:age::int AS age
FROM users

What will be the output columns for a row where data contains {"id": "U123", "attributes": {"age": 30}}?

medium

A. user_id = 'U123', age = 30

B. user_id = 'id', age = 'attributes:age'

C. user_id = NULL, age = NULL

D. user_id = 'U123', age = NULL

Solution

1. Step 1: Understand Snowflake JSON extraction

   Snowflake uses colon notation data:id to access JSON keys and casting (::string, ::int) to convert types.

2. Step 2: Apply extraction to given JSON

   For {"id": "U123", "attributes": {"age": 30}}, data:id::string extracts 'U123', and data:attributes:age::int extracts 30.

3. Final Answer:

   user_id = 'U123', age = 30 -> Option A

4. Quick Check:

   Snowflake JSON path extraction = correct values [OK]

Hint: Use colon paths and cast to get JSON values in Snowflake [OK]

Common Mistakes:

• Confusing JSON keys with strings
• Not casting extracted values
• Using wrong path syntax

4. You wrote this dbt SQL code to extract a nested JSON value:

SELECT
  data->'user'->>email AS email
FROM users

But you get an error. What is the likely cause?

medium

A. Using ->> after -> on a JSON object without parentheses

B. Using -> instead of ->> for text extraction

C. Missing quotes around 'email' key

D. The JSON column 'data' is not valid JSON

Solution

1. Step 1: JSON path operators require quoted keys

   Keys in -> and ->> must be string literals enclosed in single quotes.

2. Step 2: Identify syntax issue

   In data->'user'->>email, email lacks quotes and is interpreted as an identifier (e.g., column name) instead of a JSON key, causing a syntax error. Correct syntax: data->'user'->>'email'.

3. Final Answer:

   Missing quotes around 'email' key -> Option C

4. Quick Check:

   JSON keys must be quoted strings [OK]

Hint: Always quote JSON keys in -> and ->> operators [OK]

Common Mistakes:

• Forgetting parentheses in chained JSON extraction
• Misquoting JSON keys
• Assuming JSON column is invalid without checking

5. You have a JSON column info in a dbt model with records like:

{"user": {"id": "U1", "tags": ["a", "b", "c"]}}

How would you write a dbt SQL expression to create a new row for each tag using PostgreSQL JSON functions?

hard

A. SELECT user_id, tag FROM table_name, json_array_elements_text(info->'user'->'tags') AS tag;

B. SELECT user_id, unnest(info->'user'->'tags') AS tag FROM table_name;

C. SELECT user_id, json_each_text(info->'user'->'tags') AS tag FROM table_name;

D. SELECT user_id, explode(info->'user'->'tags') AS tag FROM table_name;

Solution

1. Step 1: Identify function to expand JSON arrays in PostgreSQL

   PostgreSQL uses json_array_elements_text() to turn JSON arrays into a set of text rows.

2. Step 2: Apply function to nested JSON array

   Extract the 'tags' array with info->'user'->'tags' and expand using comma syntax in FROM: SELECT user_id, tag FROM table_name, json_array_elements_text(info->'user'->'tags') AS tag.

3. Final Answer:

   SELECT user_id, tag FROM table_name, json_array_elements_text(info->'user'->'tags') AS tag; -> Option A

4. Quick Check:

   Use json_array_elements_text() to expand JSON arrays [OK]

Hint: Use FROM table, json_array_elements_text(...) AS tag to explode JSON arrays in PostgreSQL [OK]

Common Mistakes:

• Using unnest() on JSON instead of arrays
• Using json_each_text() which is for objects
• Using explode() which is not PostgreSQL function