Why Semi-structured data handling (JSON) in dbt? - Purpose & Use Cases

The Big Idea

What if you could turn messy JSON data into clear insights with just a few lines of code?

The Scenario

Imagine you receive a huge file full of customer feedback, but the data is messy and nested inside JSON strings. You try to open it in a spreadsheet, but everything looks jumbled and hard to read.

The Problem

Manually opening and editing JSON data in spreadsheets or text editors is slow and confusing. It's easy to make mistakes, miss important details, and waste hours trying to organize the data by hand.

The Solution

Using dbt to handle semi-structured JSON data lets you automatically extract, transform, and organize nested information into clear tables. This saves time and reduces errors by letting the computer do the heavy lifting.

Before vs After

✗ Before

SELECT raw_json FROM feedback_table;
-- Then manually parse JSON outside the database

✓ After

SELECT
  json_extract_path_text(raw_json, 'customer', 'name') AS customer_name,
  json_extract_path_text(raw_json, 'feedback', 'rating') AS rating
FROM feedback_table;

What It Enables

It enables you to turn complex, nested JSON data into simple, easy-to-analyze tables with just a few lines of code.

Real Life Example

A marketing team receives product reviews in JSON format from multiple sources. Using dbt, they quickly extract ratings and comments to analyze customer satisfaction trends without manual data cleaning.

Key Takeaways

Manual handling of JSON data is slow and error-prone.

dbt automates parsing and transforming nested JSON into tables.

This makes data analysis faster, easier, and more reliable.

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