JSON and the Amazing Technicolor Dreamcoat

Tech-savvy beaver swims up with a tablet. Greetings! Our pond location data is stored in modern JSON format instead of CSV. JSON handles complex, nested information beautifully! Can you read this JSON file directly?

Your task

Read and display specific fields from the external JSON file 'data/pond_locations.json'. Show the pond_id, pond_name, capacity, water_type, and depth_meters fields. You will be working with a JSON file containing pond location data.

Hint

Similar to CSV reading, but look for a function specifically designed for JSON format.

Expected result

A table showing pond_id, pond_name, capacity, water_type, and depth_meters from the pond_locations.json file.

Tables

`duck_diaries` (10 rows)

column	type
`diary_id`	BIGINT
`duck_id`	BIGINT
`entry_date`	DATE
`diary_text`	VARCHAR
`mood`	VARCHAR

`duck_surveys` (5000 rows)

column	type
`survey_id`	BIGINT
`duck_id`	BIGINT
`age`	BIGINT
`favorite_food`	VARCHAR
`favorite_pond`	BIGINT
`swimming_rating`	BIGINT
`migration_preference`	VARCHAR
`social_behavior`	VARCHAR
`health_status`	VARCHAR
`activity_level`	VARCHAR
`survey_date`	DATE
`survey_location`	BIGINT

`ducks` (15 rows)

column	type
`id`	BIGINT
`name`	VARCHAR
`age`	BIGINT
`species`	VARCHAR
`birth_date`	DATE
`arrival_date`	DATE

`ponds` (3 rows)

column	type
`pond_id`	BIGINT
`pond_name`	VARCHAR
`location_lat`	DOUBLE
`location_lng`	DOUBLE
`capacity`	BIGINT
`features`	VARCHAR[]
`amenities`	VARCHAR[]

Files

`data/pond_locations.json` (3 rows)

column	type
`pond_id`	BIGINT
`pond_name`	VARCHAR
`location`	STRUCT(latitude DOUBLE, longitude DOUBLE, features VARCHAR[])
`capacity`	BIGINT
`amenities`	VARCHAR[]
`water_type`	VARCHAR
`depth_meters`	DOUBLE
`is_public`	BOOLEAN
`maintenance_schedule`	VARCHAR