A self-hosted data analysis platform — structured recipe builder with AI-guided data preparation, and multi-database analytics. Open source. Runs on your infrastructure. Connects to PostgreSQL, MySQL, SQL Server, and MS Access.
Learn MoreStatistician's Workbench is a self-hosted web application that lets you connect to your databases, import files, build data preparation pipelines with a structured recipe editor or an AI assistant, and run analytics — all through a browser. Your data stays on your server. No cloud, no third-party processing, no data leaving your network.
Build data preparation pipelines step by step — chain stages like filter, join, aggregate, pivot, and impute. Each stage previews its output so you can see exactly what your data looks like at every step.
An AI assistant guides you through building a recipe — it sees your actual schema and data, then suggests which stages to use and how to configure them. Every suggestion is recorded alongside your decisions for a full audit trail. Connect to cloud AI providers or run a local LLM for total privacy.
Connect to PostgreSQL, MySQL, SQL Server, and MS Access. Browse schemas, preview tables, and run queries across all your databases from a single interface.
Import CSV, JSON, and Excel files with automatic type detection. Preview data before import. Map columns, set types, and load directly into your database.
Visualise results with bar charts, line charts, scatter plots, and histograms. Charts update as you refine your recipe — instant feedback on every change.
Open any dataset as a Jupyter notebook for advanced analysis. Python, R, and SQL kernels — write code, generate reports, and share results, all within the Workbench.
Full interface translation across multiple languages including Thai. Switch languages from the globe icon — no restart required.
Multi-user support with role-based access control. Group admins manage their own users. Fine-grained permissions on datasets, recipes, and database connections.
Runs on your own server — a standard Linux machine with Python and PostgreSQL. Deploy directly. No cloud dependency. Your infrastructure, your control.
| Site | Description |
|---|---|
| Workbench Win | The Statistician's Workbench packaged for Windows — one installer, runs locally |
| Workbench Mac | The Statistician's Workbench packaged for macOS — one installer, runs locally |
| Workbench Linux | The Statistician's Workbench for Linux — a single Debian package, installed in minutes |
| Workbench (open source) | The original Statistician's Workbench |
The Workbench is a web application built with a Python backend (FastAPI) and a Next.js frontend. Deploy it on your server and access it through any modern browser:
| Step | What Happens |
|---|---|
| 1. Deploy | Clone the repository. Point it at a PostgreSQL database. The Workbench creates its own schema for user accounts, recipes, and datasets. |
| 2. Connect | Add your database connections — PostgreSQL, MySQL, SQL Server, or MS Access. The Workbench uses native drivers (psycopg2, pymysql, pymssql, pymsaccess) to connect directly to each. |
| 3. Import | Upload CSV, JSON, or Excel files. Preview the data, adjust column types, and load into your database — or keep them as virtual datasets that query the file directly. |
| 4. Build | Create a recipe using the structured editor — chain stages like Filter, Join, Aggregate, Pivot, and Impute — or use the AI assistant, which suggests stages and configuration based on your actual data. Each stage shows a live preview of its output. Every AI suggestion is recorded, giving you a full audit trail. |
| 5. Analyse | Run the recipe to produce a dataset. Chart it, export it to CSV/Excel, or open it as a Jupyter notebook for deeper analysis with Python or R. |
| Database | Driver | Features |
|---|---|---|
| PostgreSQL | psycopg2 | Full support — schema browsing, table preview, SQL querying, recipe execution, data import/export |
| MySQL / MariaDB | pymysql | Full support — schema browsing, table preview, SQL querying, recipe execution |
| SQL Server | pymssql | Full support — schema browsing, table preview, SQL querying, recipe execution. Feature-gated behind mssql_db licence flag |
| MS Access | pymsaccess | Schema browsing, table preview, SQL querying. Read-only access to .accdb and .mdb files |
| DB2 | Planned | IBM Db2 support — planned for the Business Edition. Schema browsing, table preview, SQL querying, recipe execution. |
| IMS / Mainframe | Planned | IBM IMS and other mainframe data sources — planned for the Business Edition. Enterprise data integration for legacy systems. |
The platform has full support for 20 languages.
| Code | Language | Native Name |
|---|---|---|
en | English | English |
zh | Chinese | 中文 |
th | Thai | ไทย |
de | German | Deutsch |
ar | Arabic | العربية |
fa | Farsi | فارسی |
bn | Bengali | বাংলা |
pa | Punjabi | ਪੰਜਾਬੀ |
es | Spanish | Español |
ja | Japanese | 日本語 |
fr | French | Français |
pt | Portuguese | Português |
it | Italian | Italiano |
ko | Korean | 한국어 |
ru | Russian | Русский |
hi | Hindi | हिन्दी |
nl | Dutch | Nederlands |
tr | Turkish | Türkçe |
id | Indonesian | Bahasa Indonesia |
ur | Urdu | اردو |
The Workbench is available in four editions. The base edition is open source. The other three are licensed products, free to use in Demo mode:
| Product | License | Notes |
|---|---|---|
| Workbench | Open Source | The community edition. Full platform — PostgreSQL backend, recipe engine, statistics, charts, multilingual UI. Free to use, modify, and self-host. This is the core of the project. |
| Workbench Linux | Licensed (free) | The self-hosted Linux edition — a single Debian package. Free to use in Demo mode, which is fully functional except for a persistent banner and no support for local AI servers. workbench-linux.sentuny.com |
| Workbench Win | Licensed (free) | The Windows desktop application — same platform, easy install. Free to use in Demo mode, which is fully functional except for a persistent banner and no support for local AI servers. workbench-win.sentuny.com |
| Workbench Mac | Licensed (free) | The macOS desktop application — same easy-install approach as Workbench Win. Free to use in Demo mode, which is fully functional except for a persistent banner and no support for local AI servers. workbench-mac.sentuny.com |
| Component | Requirement |
|---|---|
| Operating System | Linux (Ubuntu 20.04+, Debian 11+, or similar). Also runs on macOS for development. |
| Python | 3.10+ with FastAPI, SQLAlchemy, and database drivers |
| Database | PostgreSQL 12+ (required — stores Workbench metadata, user accounts, recipes, datasets) |
| Node.js | 18+ for the Next.js frontend |
| Memory | 2 GB RAM minimum, 4 GB recommended |
| Disk | ~1 GB for application + dependencies. Additional space for imported datasets. |
| Browser | Any modern browser — Chrome, Edge, Firefox, Safari |
| Target Databases (optional) | PostgreSQL 12+, MySQL 8+, SQL Server 2019+, or MS Access 2010+ via the MS Access Endpoint |