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duckdb-analytics

Query Parquet files and S3 data lakes with DuckDB for OLAP analytics.

Updated Feb 2, 2026
Search Tags:#analytics#s3#data warehouse#parquet#duckdb#bi#olap
One-click install
npx skills add https://github.com/JNZader-Vault/project-starter-framework --skill duckdb-analytics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: duckdb-analytics
Source: https://github.com/JNZader-Vault/project-starter-framework/tree/main/.ai-config/skills/data-ai/duckdb-analytics
Command: npx skills add https://github.com/JNZader-Vault/project-starter-framework --skill duckdb-analytics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a powerful and efficient way to perform Online Analytical Processing (OLAP) on large datasets stored in Parquet files, including those on S3, enabling rapid business intelligence and machine learning insights.

Core Features & Use Cases

  • Columnar Querying: Optimized for analytical queries on columnar data formats like Parquet.
  • S3/MinIO Integration: Seamlessly query data lakes stored on object storage.
  • BI & ML Ready: Directly supports Business Intelligence tools and Machine Learning feature engineering.
  • Use Case: Analyze terabytes of sensor data stored in Parquet files on S3 to identify performance trends and anomalies over the past year.

Quick Start

Use the duckdb-analytics skill to query average sensor values from Parquet files in an S3 bucket.

Frequently Asked Questions about duckdb-analytics

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

FAQPage Schema
How do I run OLAP analytics on Parquet files stored in an S3 data lake?▼

OLAP analytics on S3 data lakes uses DuckDB to directly query Parquet files in object storage. This approach enables rapid business intelligence and machine learning insights without requiring a separate data warehouse.

Can I query S3 data lakes directly for BI and ML workloads using Python and Go?▼

Yes, querying S3 data lakes for BI and ML workloads supports Python and Go integrations. These integrations allow you to perform analytical queries and time-series analysis on Parquet files stored in MinIO or S3.

What is the best way to analyze large datasets in Parquet format for time-series analysis?▼

The best way to analyze large Parquet datasets for time-series analysis is using DuckDB for columnar querying. It is optimized for fast analytical query performance on warm data stores, handling terabytes of sensor data efficiently.

Does DuckDB work with MinIO for data warehousing and columnar querying?▼

Yes, DuckDB works with MinIO for data warehousing and columnar querying. It seamlessly queries data lakes stored on object storage, supporting fast analytical query performance directly on Parquet files.

When do I need DuckDB for analytical queries instead of a traditional data warehouse?▼

You need DuckDB for analytical queries when performing fast OLAP on Parquet files and S3 data lakes. It satisfies requirements for rapid analytical query performance on warm data stores without the overhead of a traditional data warehouse.

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