THE CLOUD EXPEDITION! · Chapter 5
Parquet-ly Cloudy
Arias eyes go wide behind her spectacles. Forget CSV — Parquet is the FORMAT OF LEGENDS. Columnar storage, built-in compression, blazing-fast scans. She fans herself with a schema diagram. DuckDB devours Parquet files straight from cloud storage — point it at a URL and it reads only the columns you ask for. Time to discover Parquet Files — reading columnar data directly from cloud storage.
Your task
Use `read_parquet()` to read the 2025 regional census archive and summarize total population by region. Return <b>region</b> and <b>total_population</b>, most populated regions first. File: `https://data.dbquacks.com/archives/census_2025.parquet` (public Parquet). Columns include `region` and `population`.<br><br><div class="text-xs text-gray-400 mt-2">Regional tip: MotherDuck runs in US East, US West, and EU Central. For best performance, query data stored in your region. This file is hosted in US East.</div>
Hint
read_parquet() accepts an https:// URL directly — no need to download or attach the file
Expected result
Rows with columns: region and total_population, one per region, ordered by total_population descending
Tables
lake_visits (500 rows)
| column | type |
|---|---|
visit_id | BIGINT |
duck_id | BIGINT |
destination_pond | VARCHAR |
visit_date | DATE |
purpose | VARCHAR |
pondville_events (50 rows)
| column | type |
|---|---|
event_id | BIGINT |
event_name | VARCHAR |
event_date | DATE |
organizer_id | BIGINT |
location | VARCHAR |
attendee_count | BIGINT |
pondville_residents (100 rows)
| column | type |
|---|---|
duck_id | BIGINT |
name | VARCHAR |
species | VARCHAR |
home_pond | VARCHAR |
age | BIGINT |
occupation | VARCHAR |
joined_date | DATE |
Database: https://dbquacks.com/data/databases/motherduck_tutorial.duckdb
This page as markdown: https://dbquacks.com/motherduck/13.md