Getting Started

This guide walks you through installing the core Noon tools, running your first DuckDB query, and profiling data with FineType.

Prerequisites

Noon tools run on macOS, Linux, and Windows (via WSL).

Tool	Purpose	Required
Nushell	Shell for data workflows	Recommended
DuckDB	In-memory analytical SQL engine	Yes
FineType	Semantic type classification	Yes

You’ll need a terminal and a package manager. The examples below use Homebrew (macOS/Linux), but alternatives are listed for each platform.

Install the Tools

1. Nushell

Nushell is a modern shell that treats data as structured tables — ideal for piping between tools.

# macOS / Linux
brew install nushell

# Windows
winget install nushell

# Or via Cargo (any platform with Rust)
cargo install nu

Verify:

nu --version

2. DuckDB

DuckDB is a fast, embeddable SQL engine for analytics. It reads CSV, JSON, and Parquet natively.

# macOS
brew install duckdb

# Linux (apt)
sudo apt install duckdb

# Windows
winget install DuckDB.cli

Verify:

duckdb --version

3. FineType

FineType classifies text into 152 semantic types with a character-level CNN model.

# macOS (Homebrew)
brew install noon-org/tap/finetype

# Any platform with Rust
cargo install finetype-cli

Verify:

finetype --version

Your First Workflow

Let’s walk through a complete analytics workflow: create a dataset, query it with DuckDB, then profile the column types with FineType.

Step 1: Create a Sample CSV

Save this as contacts.csv:

id,name,email,created_at,ip_address,amount
1,Alice Chen,[email protected],2024-01-15T09:30:00Z,192.168.1.10,149.99
2,Bob Smith,[email protected],2024-02-20T14:15:00Z,10.0.0.42,2500.00
3,Carol Wu,[email protected],2024-03-08T11:00:00Z,172.16.0.1,89.50
4,Dan Reeves,[email protected],2024-04-12T16:45:00Z,192.168.0.5,1200.00
5,Eve Nakamura,[email protected],2024-05-01T08:00:00Z,10.10.10.1,340.75

This dataset has a mix of types: names, emails, timestamps, IP addresses, and numeric amounts.

Step 2: Query with DuckDB

Open a DuckDB shell and explore the data:

-- Start DuckDB
duckdb

-- Load and inspect
SELECT * FROM 'contacts.csv';

-- Aggregate query
SELECT
    count(*) AS total_contacts,
    avg(amount) AS avg_amount,
    min(created_at) AS earliest,
    max(created_at) AS latest
FROM 'contacts.csv';

Expected output:

┌─────────────────┬────────────┬──────────────────────┬──────────────────────┐
│ total_contacts  │ avg_amount │      earliest        │       latest         │
│      int64      │   double   │      varchar         │      varchar         │
├─────────────────┼────────────┼──────────────────────┼──────────────────────┤
│               5 │     856.05 │ 2024-01-15T09:30:00Z │ 2024-05-01T08:00:00Z │
└─────────────────┴────────────┴──────────────────────┴──────────────────────┘

DuckDB automatically reads the CSV and lets you query it immediately — no schema definition needed.

Step 3: Profile with FineType

Now let’s see what FineType detects in each column:

finetype profile -f contacts.csv

Expected output:

Column          Type                              Confidence
──────────────  ────────────────────────────────  ──────────
id              representation.numeric.increment  0.95
name            identity.person.full_name         0.92
email           identity.person.email             0.99
created_at      datetime.timestamp.iso_8601       0.98
ip_address      technology.internet.ip_v4         0.97
amount          representation.numeric.decimal    0.94

FineType identifies semantic types beyond what SQL type inference gives you — it distinguishes emails from strings, IP addresses from text, and ISO timestamps from generic dates.

Step 4: Classify Individual Values

You can also classify single values:

finetype infer -i "[email protected]"
# → identity.person.email

finetype infer -i "192.168.1.10"
# → technology.internet.ip_v4

finetype infer -i "2024-01-15T09:30:00Z"
# → datetime.timestamp.iso_8601

Each prediction is a transformation contract — it maps to a DuckDB SQL expression guaranteed to parse the value correctly.

Bonus: DuckDB + FineType Extension

If you have the FineType DuckDB extension installed, you can classify directly in SQL:

INSTALL finetype FROM community;
LOAD finetype;

SELECT
    column_name,
    finetype(value) AS semantic_type
FROM 'contacts.csv';

What’s Next?

Keep exploring

Dive deeper into the Noon analytics ecosystem.

FineType Docs

Explore the full taxonomy, CLI commands, DuckDB extension, and performance benchmarks. Read more →

DuckDB

Learn more about DuckDB’s SQL dialect, file format support, and extensions. Documentation →

Nushell

Discover Nushell’s structured data pipelines and how they complement SQL workflows. The Nushell Book →