Bokeh vs Tableau

Overview

Tableau

Stacks1.3K

Followers1.4K

Votes8

Bokeh

Stacks95

Followers183

Votes12

GitHub Stars20.2K

Forks4.2K

Bokeh vs Tableau: What are the differences?

<Write Introduction here>

Integration with Python Programming Language: Bokeh is a Python library that provides interactive visualization capabilities for the web, making it ideal for users familiar with Python for data analysis. On the other hand, Tableau is a standalone software that offers a user-friendly interface for creating visually appealing data visualizations without the need for programming skills.
Open Source vs. Proprietary Software: Bokeh is an open-source library, allowing users to freely access and modify the source code to cater to their specific needs. Tableau, on the other hand, is proprietary software that requires a license for full functionality, limiting the flexibility and customization options available to users.
Scalability and Performance: Bokeh is known for its scalability, enabling users to create complex and interactive visualizations efficiently. Conversely, Tableau may face performance issues when dealing with large datasets or complex visualizations, impacting the overall user experience.
Community Support and Ecosystem: Bokeh benefits from a robust community of developers and contributors who continually enhance its features and provide support through forums and documentation. While Tableau also has a community of users, the level of customizability and extensibility offered by Bokeh surpasses that of Tableau.
Mapping Capabilities: Bokeh excels in geospatial visualization with its high-quality mapping capabilities, allowing users to create interactive maps with ease. Tableau, while capable of creating maps, may lack the advanced geospatial features and customization options available in Bokeh.
Real-time Data Streaming: Bokeh provides support for real-time data streaming and updating visualizations dynamically, making it suitable for applications that require real-time data analysis. In contrast, Tableau may require manual data refreshes or lack the seamless integration for real-time data visualization.

In Summary, Bokeh and Tableau differ in their integration with Python, open-source vs. proprietary nature, scalability, community support, mapping capabilities, and real-time data streaming features.

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CLI (Node.js)

Manual

Advice on Tableau, Bokeh

Vojtech

Head of Data at Mews

Nov 24, 2019

Decided

Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.

And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.

Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.

353k views353k

Comments

Wei

CTO at Flux Work

Jan 8, 2020

Decided

Very easy-to-use UI. Good way to make data available inside the company for analysis.

Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.

Can be embedded into product to provide reporting functions.

Support team are helpful.

The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.

230k views230k

Comments

Detailed Comparison

Tableau	Bokeh
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.	Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.
Connect to data on prem or in the cloud—whether it’s big data, a SQL database, a spreadsheet, or cloud apps like Google Analytics and Salesforce. Access and combine disparate data without writing code. Power users can pivot, split, and manage metadata to optimize data sources. Analysis begins with data. Get more from yours with Tableau.; Exceptional analytics demand more than a pretty dashboard. Quickly build powerful calculations from existing data, drag and drop reference lines and forecasts, and review statistical summaries. Make your point with trend analyses, regressions, and correlations for tried and true statistical understanding. Ask new questions, spot trends, identify opportunities, and make data-driven decisions with confidence.; Answer the “where” as well as the “why.” Create interactive maps automatically. Built-in postal codes mean lightning-fast mapping for more than 50 countries worldwide. Use custom geocodes and territories for personalized regions, like sales areas. We designed Tableau maps specifically to help your data stand out.; Ditch the static slides for live stories that others can explore. Create a compelling narrative that empowers everyone you work with to ask their own questions, analyzing interactive visualizations with fresh data. Be part of a culture of data collaboration, extending the impact of your insights.	interactive visualization library ; versatile graphics ; open source; https://github.com/bokeh/bokeh
Statistics
GitHub Stars -	GitHub Stars 20.2K
GitHub Forks -	GitHub Forks 4.2K
Stacks 1.3K	Stacks 95
Followers 1.4K	Followers 183
Votes 8	Votes 12
Pros & Cons
Pros 6 Capable of visualising billions of rows 1 Responsive 1 Intuitive and easy to learn Cons 3 Very expensive for small companies	Pros 12 Beautiful Interactive charts in seconds
Integrations
No integrations available	Bootstrap Flask NGINX React Django Python Jupyter Tornado Streamlit

What are some alternatives to Tableau, Bokeh?

D3.js

It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework.

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Highcharts

Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.

Plotly.js

It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Chart.js

Visualize your data in 6 different ways. Each of them animated, with a load of customisation options and interactivity extensions.

Recharts

Quickly build your charts with decoupled, reusable React components. Built on top of SVG elements with a lightweight dependency on D3 submodules.

ECharts

It is an open source visualization library implemented in JavaScript, runs smoothly on PCs and mobile devices, and is compatible with most current browsers.

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

ZingChart

The most feature-rich, fully customizable JavaScript charting library available used by start-ups and the Fortune 100 alike.

Related Comparisons

Bokeh vs Tableau: What are the differences?

<Write Introduction here>

Integration with Python Programming Language: Bokeh is a Python library that provides interactive visualization capabilities for the web, making it ideal for users familiar with Python for data analysis. On the other hand, Tableau is a standalone software that offers a user-friendly interface for creating visually appealing data visualizations without the need for programming skills.
Open Source vs. Proprietary Software: Bokeh is an open-source library, allowing users to freely access and modify the source code to cater to their specific needs. Tableau, on the other hand, is proprietary software that requires a license for full functionality, limiting the flexibility and customization options available to users.
Scalability and Performance: Bokeh is known for its scalability, enabling users to create complex and interactive visualizations efficiently. Conversely, Tableau may face performance issues when dealing with large datasets or complex visualizations, impacting the overall user experience.
Community Support and Ecosystem: Bokeh benefits from a robust community of developers and contributors who continually enhance its features and provide support through forums and documentation. While Tableau also has a community of users, the level of customizability and extensibility offered by Bokeh surpasses that of Tableau.
Mapping Capabilities: Bokeh excels in geospatial visualization with its high-quality mapping capabilities, allowing users to create interactive maps with ease. Tableau, while capable of creating maps, may lack the advanced geospatial features and customization options available in Bokeh.
Real-time Data Streaming: Bokeh provides support for real-time data streaming and updating visualizations dynamically, making it suitable for applications that require real-time data analysis. In contrast, Tableau may require manual data refreshes or lack the seamless integration for real-time data visualization.

In Summary, Bokeh and Tableau differ in their integration with Python, open-source vs. proprietary nature, scalability, community support, mapping capabilities, and real-time data streaming features.

Bokeh vs Tableau

Overview