THE CLOUD EXPEDITION! · Chapter 5
The CSV Scrolls
Archivist Aria pushes her spectacles up with trembling excitement. I found historical records scattered across the web — raw CSV files, just sitting there on public servers! She cracks her knuckles over the keyboard. With httpfs, DuckDB can read them directly from any URL. No downloading, no importing — its basically telekinesis for data. Master HTTP File Reads — querying remote files directly by…
Your task
Use `read_csv()` to query the historical pond discoveries archive directly from its URL — no download needed. Filter for discoveries after 1950, ordered by year. File: `https://data.dbquacks.com/archives/pond_discoveries.csv` (remote CSV).<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. These files are hosted in US East.</div>
Hint
Use read_csv() with the URL as your table source — you can filter and sort it like any normal table
Expected result
Rows with columns like pond_name, discoverer_name, and discovery_year for ponds discovered after 1950
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/12.md