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Streamsync VS Bokeh

Compare Streamsync VS Bokeh and see what are their differences

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Streamsync logo Streamsync

Streamsync is an open-source framework for creating data apps. Build user interfaces using a visual editor; write the backend code in Python.

Bokeh logo Bokeh

Bokeh visualization library, documentation site.
  • Streamsync Landing page
    Landing page //
    2023-05-04

It's fast.

  • Streamsync enables significantly lower response times, when compared to Streamlit.
  • It only runs the user script once.
  • It uses WebSockets to keep frontend and backend states in sync.

It's neat.

  • Streamsync uses state-driven, reactive user interfaces. A data app's user interface is strictly separated from its logic.
  • It uses a consistent yet customisable UI design system.
  • No caching needed; the script runs once and things remain in memory. You can use globals and module attributes to store app-wide data.
  • Predictable flow of execution. Event handlers are plain, easily testable Python functions. No re-runs, no strange decorators.

Check out a live demo of an app.

  • Bokeh Landing page
    Landing page //
    2022-11-01

Streamsync features and specs

No features have been listed yet.

Bokeh features and specs

  • Interactive Visualizations
    Bokeh is designed specifically for creating interactive and highly customizable visualizations, making it suitable for engaging data exploration.
  • Python Integration
    Bokeh integrates well with the Python ecosystem, allowing direct use of pandas, NumPy, and other Python libraries, facilitating seamless data manipulation and visualization.
  • Web Compatibility
    Bokeh generates plots that are ready to be embedded into web applications, making it a powerful tool for creating dashboards and interactive reports.
  • Server Functionality
    Bokeh provides a server component that allows users to build and deploy sophisticated interactive applications using just Python.
  • Variety of Plotting Options
    Bokeh offers a wide range of plotting capabilities including charts, maps, and streamgraphs, enabling users to create complex visual stories.

Possible disadvantages of Bokeh

  • Learning Curve
    Bokeh may have a steeper learning curve for users unfamiliar with JavaScript or those looking for a very simple or quick plotting tool.
  • Performance Issues
    When dealing with very large datasets, Bokeh might suffer from performance issues, as it is primarily client-side rendering.
  • Limited 3D Capabilities
    Bokeh's support for 3D plotting is limited compared to other visualization libraries like Plotly, potentially restricting its use for applications that require 3D visualizations.
  • Documentation and Community Size
    While Bokeh has good documentation, its user community is smaller compared to more mature libraries like Matplotlib, which can mean fewer resources and third-party support options.

Streamsync videos

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Bokeh videos

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"Bokeh" - Netflix Film Review

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Category Popularity

0-100% (relative to Streamsync and Bokeh)

Streamsync

Bokeh

Developer Tools
100 100%
Developer Tools
0% 0
Charting Libraries
0 0%
Charting Libraries
100% 100
Application And Data
100 100%
Application And Data
0% 0
Data Visualization
0 0%
Data Visualization
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Streamsync and Bokeh

Streamsync Reviews

We have no reviews of Streamsync yet.
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Bokeh Reviews

Top 8 Python Libraries for Data Visualization
Pygal is a Python data visualization library that is made for creating sexy charts! (According to their website!) While Pygal is similar to Plotly or Bokeh in that it creates data visualization charts that can be embedded into web pages and accessed using a web browser, a primary difference is that it can output charts in the form of SVG’s or Scalable Vector Graphics. These...
Source: www.geeksforgeeks.org

Social recommendations and mentions

Based on our record, Bokeh should be more popular than Streamsync. It has been mentiond 5 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Streamsync mentions (2)

Bokeh mentions (5)

  • [OC] Chemical Diversity of The GlobalChem Common Chemical Universe
    Visualization: https://docs.bokeh.org/en/latest/. Source: about 3 years ago
  • Profiling workflows with the Amazon Genomics CLI
    Now that we can get task timing information in a consistent manner, let’s do some plotting. For this, I’m going to use Bokeh which generates nice interactive plots. - Source: dev.to / about 3 years ago
  • 10 Python Libraries For Data Visualization
    Bokeh The Bokeh library is native to Python and is mainly used to create interactive, web-ready plots, which can be easily output as HTML documents, JSON objects, or interactive web applications. Like ggplot, its concepts are also based on the Grammar of Graphics. It has the added advantage of managing real-time data and streaming. This library can be used for creating common charts such as histograms, bar plots,... - Source: dev.to / over 3 years ago
  • Graphic library Bokeh is underrated and underdocumented
    It's not in the least bit "underrated" and it's documentation is extensive. Source: almost 4 years ago
  • Help with Bokeh Interactive Plot
    Hi guys! I am currently working on a project to enrich my Master thesis with some interactive plots. I have been using the Bokeh library to make a standalone application, which I was then planning to deploy in Heroku. You can find the code in this repository. But I will also add it at the bottom of the post. Source: about 4 years ago

What are some alternatives?

When comparing Streamsync and Bokeh, you can also consider the following products

Streamlit - Turn python scripts into beautiful ML tools

Plotly - Low-Code Data Apps

Anvil.works - Build seriously powerful web apps with all the flexibility of Python. No web development experience required.

RAWGraphs - RAWGraphs is an open source app built with the goal of making the visualization of complex data...

Panel - High-level app and dashboarding solution for Python

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

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