RATH is beyond an open-source alternative to Data Analysis and Visualization tools such as Tableau. It automates your Exploratory Data Analysis workflow with an Augmented Analytic engine by discovering patterns, insights, causals and presents those insights with powerful auto-generated multi-dimensional data visualization.
RATH is a cutting-edge platform designed to automate and enhance exploratory data analysis (EDA) workflows. Built beyond traditional tools like Tableau, RATH leverages an augmented analytics engine to discover patterns, insights, and causal relationships in data automatically. It presents these findings through powerful, auto-generated multi-dimensional visualizations, making complex datasets more accessible and actionable.
Key Features:
Automated discovery of hidden patterns and insights.
Identification of causal relationships within data.
Multi-dimensional visualization capabilities for enhanced clarity.
RATH is not just an open-source alternative to Data Analysis and Visualization tools such as Tableau, but it automates your Exploratory Data Analysis workflow with an Augmented Analytic engine by discovering patterns, insights, causals and presents those insights with powerful auto-generated multi-dimensional data visualization.
> RATH generates/recommends visualizations based on minimize visual perception error of information in visualizations.
> [!TIP]
> If you want more AI features, we also build , an AI Code Agent in Jupyter that understands your code/data/cells and generate code, execute cells and take actions for you. It can be used in jupyter lab with
Integration with various data sources to streamline analysis.
Collaboration tools for teams to share insights effectively.
Open-source flexibility for customization and extension.
Audience & Benefit:
Ideal for data analysts, scientists, and professionals working with large datasets, RATH empowers users to uncover actionable insights faster. By automating EDA workflows, it reduces the time spent on manual analysis, enabling users to focus on deriving meaningful conclusions from their data.
RATH can be installed via winget for seamless integration into your workflow.
🤖 AutoPilot for Data Exploration: Get Insights with One Click! Augmented analytic engine for discovering patterns, insights, and causals. A fully-automated way to explore and visualize dataset with one click.
🛠 Copilot for Data Exploration: RATH will work as your copilot in data science, learn your intends and generate relevant recommendations.
Natural Language interface: Ask questions in natural language to get answers/visualizations from your data.
AutoVis: RATH will generate the best visualization for the data you selected. It makes you focus on data and variables, not how to make a visualization.
👓 Data Wrangler: Automated data wrangler for generating summary of the data and data transformation.
🎨 Data Painter: An interactive, instinctive yet powerful tool for exploratory data analysis by directly coloring your data, with further analytical features. Watch this video demonstrating about how to discover data insights with Data Painter.
:bar_chart: Dashboard: Build a beautiful interactive data dashboard (including a automated dashboard designer which can provide suggestions to your dashboard).
Causal Analysis: Identify and examine the causal relationship between variables, which can help explore the data, create better prediction models and make business decission.
Walkthroughs
Import data from online databases or CSV/JSON files.
View statistics from your data source
Data Preparation
RATH support data preparation with black magic like predictive transformation operations. It will automatically generate suggestions of transformations and cleaning, etc.
One-click automated data analysis with visualizations
Augmented analytic engine for discovering patterns, insights, and causals. A fully-automated way to explore and visualize dataset with one click.
Use RATH as your Copilot in Data Exploration
RATH will work as your copilot in data science, learn your intends and generate relevant recommendations.
Ask questions about your data, RATH integrates with GPT to generate answers and visualizations.
Manually explore your data with drag and drop:
> Manual Exploration is an independent embedding module. You can use it independently in your apps. For more details, refer to the README.md in in packages/graphic-walker/README.md.
>
> Install Graphic Walker
> bash > yarn add @kanaries/graphic-walker > # or > npm i --save @kanaries/graphic-walker >
:sparkles: Interactive data analysis workflow by data painting
Causal analysis could be defined as the way to identify and examine the causal relationship between variables, which can help explore the data, create better prediction models and make business decision.
RATH's causal analysis feature include:
Causal Discovery
Editable graphical causal models
Causal interpretability
Interactive tools for deeper exploration
What-if analysis
For more about Causal Analysis features, refer to RATH Docs.
Supported Databases
RATH supports a wide range of data sources. Here are some of the major database solutions that you can connect to RATH:
If you want to add support for more database types or data engines, feel free to Contact us
Developer Documentation
RATH software is in open alpha stage. We are working on improving its code and documentation.
build script for client parts
yarn install
yarn workspace rath-client build
If you are using RATH for your project(s), please let us know what are you using it for by emailing us at support@kanaries.org. Feedbacks are also welcomed. If you find a bug or have a feature request, please create an issue.
We encourage you to check out our RATH Docs for references and guidance.
Project Status
Community
Kanaries community is a place to have open discussions on features, voice your ideas, or get help with general questions. Get onboard with us through the following channels:
Our developer community is the backbone of the ongoing RATH project. We sincerely welcome you to join our community, participate in the conversation and stay connected with us for the latest updates.
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