DuckDB SQL on Parquet after work: query files without a warehouse

Why this matters after work

You searched for duckdb sql on parquet. After a full day of work, you do not need a 400-page manual—you need a why-first path that fits one evening: understand the angle (SQL over Parquet files for evening market research), run a tiny example, and know what not to claim.

This hub article is educational only. Not investment advice. It will not pick stocks, size positions, or promise edge.

The evening angle

Tool focus: duckdb.

• DuckDB reads Parquet in place—no copy into a heavy DB required
• push filters in SQL so column/row-group pruning stays cheap
• great for ad-hoc joins of bars, fundamentals, and calendars
• export small result sets to pandas only at the last mile

Toy code (safe, synthetic)

import duckdb
import pyarrow as pa
import pyarrow.parquet as pq
# write a tiny parquet
table = pa.table({"date": ["2024-01-02","2024-01-03","2024-01-03"],
                  "ticker": ["AAPL","AAPL","MSFT"],
                  "close": [180.0, 181.2, 370.5]})
pq.write_table(table, "/tmp/toy_bars.parquet")
con = duckdb.connect()
print(con.execute("""
  SELECT ticker, avg(close) AS avg_close
  FROM read_parquet('/tmp/toy_bars.parquet')
  WHERE ticker = 'AAPL'
  GROUP BY 1
""").fetchdf())

Run it in a throwaway venv. If an import is missing, install only what you need for tonight—do not boil the ocean.

Practice loop (35–50 minutes)

1. Recreate the toy snippet from memory (or retype it—muscle memory beats copy-paste).
2. Swap synthetic numbers for a CSV you already have (public sample or your own research extract).
3. Write three bullet notes: what worked, what broke, what you will not automate yet.
4. Commit the script or notebook with a clear message—future you will thank present you.

Pitfalls

• Treating a tutorial metric as a tradable signal.
• Leaking future information into features or labels.
• Pasting API keys, customer data, or proprietary code into random tools.
• Upgrading five libraries at once when one change would isolate the failure.

What success looks like tonight

You can explain SQL over Parquet files for evening market research in two sentences, show one chart or printout, and list one risk. That is enough progress for an after-work session.

Next step

Tomorrow, connect this tool to the adjacent skill (data hygiene, validation, or a simple dashboard)—still educational, still not investment advice.

Educational only. Not investment advice.