Profile a Parquet File

Profile data stored in Parquet format using Finetype — directly with the CLI or inside DuckDB.

Goal: Profile a Parquet file to discover the semantic types in your data, either directly with the CLI or inside the DuckDB extension.

Prerequisites

Tool	Purpose
Finetype	Semantic type detection
DuckDB	Required by `finetype profile`, which reads files through DuckDB
A `.parquet` file	Any Parquet file — a data warehouse export, a public dataset, your own data

Two ways to profile Parquet

finetype profile reads CSV and Parquet through the DuckDB engine, so a Parquet file needs no conversion step:

Profile it directly — point finetype profile at the .parquet file
Use the DuckDB extension — classify columns directly inside SQL queries

Both approaches give you the same type labels. Choose whichever fits your workflow.

Option A: Profile the file directly

Run profile on the Parquet file — the same command you would use for a CSV:

finetype profile -f data.parquet

Finetype Column Profile — "data.parquet" (1000 rows, 5 columns)
════════════════════════════════════════════════════════════════════════════════

  COLUMN                    TYPE                                      BROAD   CONF
  ──────────────────────────────────────────────────────────────────────────────
  user_id                   representation.identifier.increment      BIGINT  77.1% [numeric_sequential_detection]
  email                     identity.person.email                   VARCHAR 100.0%
  signup_date               datetime.date.iso                          DATE 100.0%
  country                   geography.location.country              VARCHAR 100.0%
  ip_address                technology.internet.ip_v4               VARCHAR 100.0% [ipv4_detection]

5/5 columns typed, 1000 rows analyzed

You now know the semantic types in your Parquet file. From here you can export a schema (finetype profile -f data.parquet -o json-schema), validate and materialise a typed table (finetype validate data.parquet schema.json --db out.db --table sample), or simply use the profile as documentation.

Option B: Use the DuckDB extension

The Finetype DuckDB extension profiles columns directly inside SQL — no CSV export needed.

1. Install and load the extension

INSTALL finetype FROM community;
LOAD finetype;

The signed community artifact loads on DuckDB 1.2 through 1.5+. See the DuckDB Extension docs for the full function reference.

2. Profile every column

Materialise the Parquet file as a table, then pass its name to the ft_profile table macro:

CREATE TABLE data AS SELECT * FROM read_parquet('data.parquet');

FROM ft_profile('data');

┌─────────────┬─────────────────────────────────────┬────────────────────┬─────────────┐
│ column_name │                type                 │     confidence     │ duckdb_type │
├─────────────┼─────────────────────────────────────┼────────────────────┼─────────────┤
│ amount      │ finance.currency.amount             │ 0.9960123300552368 │ VARCHAR     │
│ created_at  │ datetime.timestamp.iso_8601         │ 0.9695983529090881 │ TIMESTAMP   │
│ email       │ identity.person.email               │                1.0 │ VARCHAR     │
│ id          │ representation.identifier.increment │ 0.9105001091957092 │ BIGINT      │
│ ip_address  │ technology.internet.ip_v4           │                1.0 │ INET        │
│ name        │ identity.person.full_name           │ 0.9888034462928772 │ VARCHAR     │
└─────────────┴─────────────────────────────────────┴────────────────────┴─────────────┘

ft_profile returns one row per column — the detected type, confidence, and the DuckDB type to cast to — the equivalent of finetype profile running entirely inside DuckDB.

Because the result is an ordinary relation, you can filter it inline — for example, to surface only the columns that warrant a typed cast:

SELECT column_name, type, duckdb_type
FROM ft_profile('data')
WHERE duckdb_type <> 'VARCHAR';

What you learned

finetype profile reads Parquet directly — the same command as for CSV, no conversion step
The DuckDB extension's ft_profile table macro profiles columns in-place — useful when you want to stay in SQL
Both paths produce the same Finetype type labels