Rath

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Introduction

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.

https://user-images.githubusercontent.com/22167673/234053551-24e0f1c9-1efb-4250-a2f8-dbf148f5f4d0.mp4

[!TIP] If you want more AI features, we also build runcell, 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 pip install runcell

https://github.com/user-attachments/assets/9ec64252-864d-4bd1-8755-83f9b0396d38

Get started

Get started with RATH now!

• 🚀 Sandbox demo for parts of features
• 📖 Read RATH Docs
• Video introducing RATH's data painter

Features

• 🤖 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.

https://user-images.githubusercontent.com/22167673/234018034-c7441549-e83b-4f5e-94c7-f772527a7094.mp4

Ask about your data

Ask questions about your data, RATH integrates with GPT to generate answers and visualizations.

Manually explore your data with drag and drop:

<a href="https://docs.kanaries.net/rath/explore-data/explore-data-graphic-walker"><img src="https://docs-us.oss-us-west-1.aliyuncs.com/images/readme/graphic-walker.gif" alt="Manually explore your data with a Tableau-like UI"></a>

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

yarn add @kanaries/graphic-walker
# or
npm i --save @kanaries/graphic-walker

:sparkles: Interactive data analysis workflow by data painting

Data Painter Video 🔥 on Youtube

<a href="https://docs.kanaries.net/rath/explore-data/data-painter"><img src="https://docs-us.oss-us-west-1.aliyuncs.com/images/readme/data-painter.gif" alt="Interactive data analysis by painting"></a>

🌅 Causal Analysis (Alpha stage)

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:

<p align="center"> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/athena.png" alt="Amazon Athena" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/redshift.png" alt="Amazon Redshift" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/spark.png" alt="Apache Spark SQL" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/doris.png" alt="Apache Doris" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/clickhouse.png" alt="Clickhouse" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/hive.png" alt="Apache Hive" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/mysql.png" alt="MySQL" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/postgresql.png" alt="Postgre SQL" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/impala.png" alt="Apache Impala" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/kylin.png" alt="Apache Kylin" border="0" width="200" height="80"/> <img src="https://kanaries-docs.oss-cn-hangzhou.aliyuncs.com/img/github-logos/oracle.png" alt="Oracle" border="0" width="200" height="80"/>