# Data-Transport Studio

**Data-Transport Studio** is a front-end for the [data-transport](https://github.com/lnyemba/data-transport) framework and library. It lowers the barrier to entry to the capabilities of data-transport by providing an intuitive interface that makes it easy to connect to, query, and analyze data across diverse sources—without needing to write boilerplate code.

Powered by the data-transport framework and library, Data-Transport Studio adds **AI-powered querying** and an **integrated spreadsheet** to empower users to explore, transform, and better understand their data.

## What data-transport Provides

At its core, Data-Transport Studio is powered by data-transport—a lightweight abstraction layer that enables seamless, unified access to a wide variety of data environments:

- **SQL Databases:** PostgreSQL, MySQL, SQL Server, SQLite3+, DuckDB
- **NoSQL Databases:** MongoDB, CouchDB
- **Cloud & Warehouse:** S3, Nextcloud, Apache Iceberg, Apache Drill
- **Other Integrations:** Files, RabbitMQ, HTTP

## Key Features

- **Simple Connection Management:** Easily register and manage connections to diverse data sources through a label-based interface, eliminating the need to handle raw connection strings.
- **Intuitive Querying:** Query your data directly through the interface without writing Python scripts or complex boilerplate.
- **AI-Powered Data Exploration:** Translate natural language prompts into precise SQL queries and generate multi-sheet analytical reports using integrated providers like **Ollama** and **Azure OpenAI**.
- **Integrated Spreadsheet:** View query results directly in an integrated spreadsheet, enabling ad-hoc analysis, data wrangling, augmentation, and export with ease.
- **Immediate Results:** Interact with your data in real-time and iterate on analysis quickly through a spreadsheet-driven workflow.

## Why Use Data-Transport Studio?

- **Empowers Non-Technical Users:** Analysts and domain experts can explore and analyze data without needing to write code.
- **Accelerates Insights:** Natural language querying, automated analysis, and spreadsheet-based exploration help users understand their data faster.
- **Reduces Complexity:** A single interface replaces the need to juggle multiple tools, clients, or scripts to work with different data sources.
- **Focus on Analysis, Not Infrastructure:** Spend less time on connectivity and boilerplate, and more time uncovering actionable insights.

For more information on the underlying framework and library, visit the [data-transport repository](https://github.com/lnyemba/data-transport).