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duckdb

Query Parquet, CSV, and JSON files in place with DuckDB SQL.

Updated Nov 2, 2024
Search Tags:#analytics#json#sql#csv#parquet#duckdb#in-process
One-click install
npx skills add https://github.com/nrminor/.dotfiles --skill duckdb-nrminor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: duckdb
Source: https://github.com/nrminor/.dotfiles/tree/main/.config/.claude/skills/duckdb
Command: npx skills add https://github.com/nrminor/.dotfiles --skill duckdb-nrminor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the challenge of performing fast, in-database analytics directly on flat files without moving data into a separate database server. It enables analysts to query Parquet/CSV/JSON files in place, accelerating exploration and reporting.

Core Features & Use Cases

  • In-process SQL analytics on files without a separate server.
  • Read data directly from Parquet, CSV, and JSON files for quick analysis.
  • OLAP operations, aggregations, window functions, and flexible data wrangling across local datasets.
  • Use Case: You have a sample Parquet dataset and need to quickly compute summary statistics and export results without loading data into a dedicated database.

Quick Start

Install the DuckDB runtime (CLI or Python package) and run a sample query. duckdb -c "SELECT * FROM read_parquet('data.parquet') LIMIT 5;" Or use Python: import duckdb; con = duckdb.connect('example.db'); print(con.execute('SELECT 1').fetchall())

Frequently Asked Questions about duckdb

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query CSV and Parquet files with SQL without setting up a database server?▼

DuckDB enables in-process SQL analytics directly on CSV, Parquet, and JSON files without a separate server. Install the DuckDB runtime, then query files in place using standard SQL syntax—for example, `duckdb -c "SELECT * FROM read_parquet('data.parquet')"` retrieves data instantly without data migration.

Can I perform aggregations and window functions on local data files?▼

Yes. DuckDB supports OLAP operations, aggregations, and window functions directly on Parquet, CSV, and JSON files stored locally or in cloud storage. You run complex analytical queries in-process without loading data into a separate database.

What's the quickest way to explore and summarize a Parquet dataset?▼

DuckDB's in-process SQL engine lets you compute summary statistics and explore Parquet files immediately. Use the Python package (`import duckdb; con.execute('SELECT...')`) or CLI to query and export results without ETL overhead or server setup.

Do I need a database server to analyze flat files with SQL?▼

No. DuckDB eliminates server requirements by executing SQL analytics directly in your process. It reads Parquet, CSV, and JSON files on demand, making it ideal for ad-hoc exploration and data wrangling on local projects or cloud-stored datasets.

Can DuckDB handle data wrangling across multiple file formats?▼

Yes. DuckDB natively supports SQL-based data wrangling across Parquet, CSV, and JSON files in a single query. You can join, filter, transform, and aggregate data across formats without converting or moving files to a database.

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