The Zaturn server enables AI-driven data analysis and visualization directly from multiple data sources without requiring coding or manual data uploads.
Capabilities:
List available data sources and their types
List tables/datasets within a specified data source
Run SQL queries against supported data sources (PostgreSQL, MySQL, SQLite, DuckDB, ClickHouse, CSV, Parquet)
Display results of previously executed queries in markdown table format
Generate visualizations (scatter plots, line plots, histograms, strip plots, box plots, bar plots)
Customize visualizations with parameters like x-axis, y-axis, hue, bins, legends, and orientation
Provides community support and feedback through a Discord server
Connects to DuckDB databases to run SQL queries and generate insights from the data
Hosts source code and enables issue tracking for support and feature requests
Uses Kaggle datasets (specifically Pokemon dataset) for demonstration purposes
Connects to MySQL databases to run SQL queries and analyze data for business intelligence
Connects to PostgreSQL databases to execute SQL queries and extract analytical insights
Distributes the Zaturn package through the Python Package Index for installation
Connects to SQLite databases to run SQL queries and perform data analysis
Just Chat With Your Data! No SQL, No Python.
Zaturn provides tools that enable AI models to run SQL, so you don't have to. It can be used as an MCP or as a web interface similar to Jupyter Notebook.
Related MCP server: Deep Thinking Assistant
Zaturn in Action
https://github.com/user-attachments/assets/d42dc433-e5ec-4b3e-bef0-5cfc097396ab
Features:
Multiple Data Sources
Zaturn can currently connect to the following data sources:
SQL Databases: PostgreSQL, SQLite, DuckDB, MySQL, ClickHouse, SQL Server, BigQuery
Files: CSV, Parquet
Request data source by raising an issue
Visualizations
In addition to providing tabular and textual summaries, Zaturn can also generate the following image visualizations
Scatter and Line Plots
Histograms
Strip and Box Plots
Bar Plots
Density Heatmap (aka. 2D Histograms)
Polar Scatter and Line Plots
Request visualizations by raising an issue
Installation & Setup
See https://zaturn.pro/docs/install
Roadmap
Support for more data source types & more data visualizations as per community requests
Dashboard building: Pin queries and visuals to re-run without LLM calls.
Multi-user capabilities
Predictive Analytics and ML features
Help And Feedback
Support The Project
If you find Zaturn useful, please support this project by:
Starring the project
Sponsoring the development
Spreading the word
Your support will enable me to dedicate more of my time to Zaturn.
Example Dataset Credits
The pokemon dataset compiled by Sarah Taha and PokéAPI has been included under the CC BY-NC-SA 4.0 license for demonstration purposes.
Featured on glama.ai
Star History
Appeared in Searches
- Exploring and Analyzing CSV Data with Statistics, Filters, and Aggregation
- Tools and Techniques for Manipulating and Understanding CSV Files
- Assistance with Creating Plotly Graphs Using AI
- Techniques and Tools for Data Analysis, Exploration, and Working with Parquet and CSV Files
- API Documentation Resources