Semi-structured data handling (JSON) in dbt - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract the 'name' field from the JSON column 'data'.

dbt

SELECT data:[1] AS name FROM users

Drag options to blanks, or click blank then click option'

Aaddress

Bage

Cname

Demail

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot notation instead of colon for JSON extraction.

Selecting a wrong key name that doesn't exist in the JSON.

The JSON path data:name extracts the 'name' field from the JSON column.

2fill in blank

medium

Complete the code to filter rows where the JSON field 'status' equals 'active'.

dbt

SELECT * FROM users WHERE data:[1] = 'active'

Drag options to blanks, or click blank then click option'

Astatus

Btype

Clevel

Drole

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong JSON key that does not represent status.

Forgetting to use quotes around the string 'active'.

The condition data:status = 'active' filters rows where the JSON field 'status' is 'active'.

3fill in blank

hard

Fix the error in the code to correctly extract the 'city' from the nested JSON field 'address'.

dbt

SELECT data:address[1]city AS city FROM users

Drag options to blanks, or click blank then click option'

A:

B.

C->

D->>

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot notation instead of colon for nested JSON.

Using arrow operators which are not supported in dbt SQL.

In dbt SQL, use : to access nested JSON fields, so data:address:city extracts the city.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

dbt

SELECT [1] AS word, LENGTH([2]) AS length FROM words WHERE LENGTH(word) > 3

Drag options to blanks, or click blank then click option'

Aword

Bwords

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using different column names for word and length calculation.

Using table name instead of column name inside LENGTH.

The column word is selected as the word, and LENGTH(word) calculates its length.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of uppercase words and their lengths greater than 4.

dbt

SELECT [1] AS word_upper, LENGTH([2]) AS length FROM words WHERE LENGTH([3]) > 4

Drag options to blanks, or click blank then click option'

AUPPER(word)

Bword

DLOWER(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Using LOWER instead of UPPER for the first blank.

Using different columns for length and filtering.

Use UPPER(word) to get uppercase words, and word for length and filtering.

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