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duckdb

Query, transform, and analyze CSV, Parquet, and JSON files with SQL.

3|Updated Mar 6, 2026
Search Tags:#json#data analysis#sql#csv#parquet#duckdb#local data
One-click install
npx skills add https://github.com/benjaminwestern/google-engineer-skills --skill duckdb-benjaminwestern
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: duckdb
Source: https://github.com/benjaminwestern/google-engineer-skills/tree/main/skills/duckdb
Command: npx skills add https://github.com/benjaminwestern/google-engineer-skills --skill duckdb-benjaminwestern

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of querying, transforming, and analyzing local data stored in various file formats like CSV, Parquet, and JSON, eliminating the need for complex data loading into external databases.

Core Features & Use Cases

  • SQL Analytics: Perform powerful SQL queries directly on local files.
  • Data Import/Export: Seamlessly import data from and export data to CSV, Parquet, and JSON.
  • Ad Hoc Exploration: Quickly explore and understand datasets without setup.
  • Embedded Database: Use DuckDB as an in-process analytical database within scripts or pipelines.
  • Use Case: Analyze sales data from a directory of CSV files, join it with product information from a Parquet file, and export the aggregated results to a new CSV.

Quick Start

Use the duckdb skill to query all data from the file 'sales_data.csv'.

Frequently Asked Questions about duckdb

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

FAQPage Schema
How do I query local CSV and Parquet files using SQL?▼

You can query local CSV and Parquet files using SQL by leveraging an embedded analytical database to run statements directly on the files. This enables ad hoc exploration without loading data into an external database.

What is the best way to join JSON data with a CSV file for ad hoc analysis?▼

The best way to join JSON data with a CSV file is using an in-process analytical database that reads both formats directly. This allows you to execute standard SQL joins across different local file formats for ad hoc analysis.

Do I need to install a separate database server to analyze local data files?▼

No, you do not need a separate database server to analyze local data files. The process uses an embedded database running in-process within your scripts or pipelines, requiring only the CLI or Python library for execution.

Can I export transformed SQL query results back to JSON or CSV formats?▼

Yes, you can export transformed SQL query results back to JSON or CSV formats. The workflow supports seamless data import and export, allowing you to save aggregated or filtered results into new local files.

How does an embedded SQL database handle data exploration workflows?▼

An embedded SQL database handles data exploration by executing queries directly within your scripts or data pipelines. It provides a setup-free environment to quickly transform and understand local datasets.

Are there limitations when querying large Parquet files without loading them into a database?▼

Querying large Parquet files without a database is generally efficient, but requires the DuckDB CLI or Python library for execution. Complex transformations might be constrained by local memory limits during the embedded analytical process.

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