Software Alternatives & Startups

vert.x VS Plotly

Compare vert.x VS Plotly and see what are their differences

vert.x logo vert.x

From Wikipedia, the free encyclopedia

Plotly logo Plotly

Low-Code Data Apps
  • vert.x Landing page
    Landing page //
    2022-06-12
  • Plotly Landing page
    Landing page //
    2023-07-31

vert.x features and specs

  • Performance
    Vert.x is designed to be highly performant, leveraging a non-blocking, event-driven architecture which makes it suitable for handling many concurrent requests efficiently.
  • Polyglot
    Vert.x supports multiple programming languages, including Java, Kotlin, JavaScript, Groovy, Ruby, and more. This allows developers to use the language they are most comfortable with.
  • Modular
    Vert.x is modular and lightweight, enabling developers to use only the parts they need and easily integrate with other libraries and tools.
  • Reactive Ecosystem
    Vert.x provides a robust ecosystem for building reactive applications, including asynchronous APIs, event bus, and reactive streams.
  • Scalability
    The architecture of Vert.x allows for easy scaling both vertically and horizontally, as it can efficiently manage resources and load balancing.

Possible disadvantages of vert.x

  • Learning Curve
    The event-driven and asynchronous nature of Vert.x can be challenging for developers who are accustomed to traditional synchronous programming paradigms.
  • Community and Resources
    While growing, the Vert.x community is smaller compared to more established frameworks, which may result in fewer resources, tutorials, and third-party integrations.
  • Complexity
    As applications grow in size, managing asynchronous code and callback structures can become complex, requiring careful planning and architecture decisions.
  • Tooling
    Tooling support, while improving, may not be as comprehensive as other established frameworks, which might impact development speed and debugging.

Plotly features and specs

  • Interactivity
    Plotly offers highly interactive plots that allow users to pan, zoom, and hover over data points for more information. This enhances the user experience and provides deeper insights.
  • High-quality visualizations
    It provides aesthetically pleasing and highly customizable charts, making it suitable for publication-quality visuals.
  • Versatility
    Plotly supports multiple chart types including line charts, scatter plots, bar charts, and 3D plots, making it suitable for a wide range of applications.
  • Python integration
    Plotly is well-integrated with Python and works seamlessly with other popular data science libraries like Pandas, NumPy, and Scikit-learn.
  • Web-based
    The plots can be easily embedded in web applications or dashboards, making it ideal for sharing insights over the internet.
  • Open-source
    Plotly offers an open-source version, which allows users to create and share visualizations without any cost.

Possible disadvantages of Plotly

  • Performance
    Rendering very large datasets can sometimes be slow, which may not be suitable for real-time data visualization requirements.
  • Learning curve
    Even though the library is well-documented, the extensive range of features can have a steep learning curve for beginners.
  • Cost for advanced features
    While the basic functionality is free, more advanced features, such as export to certain formats and additional customizable options, require a paid subscription.
  • Dependency management
    Plotly has a number of dependencies that need to be managed properly, which can sometimes complicate the setup process.
  • Complexity
    For simple visualizations, Plotly might be overkill and simpler libraries like Matplotlib or Seaborn could be more appropriate.

Analysis of Plotly

Overall verdict

  • Overall, Plotly is a strong choice for those looking to create dynamic and interactive data visualizations, thanks to its range of features and ease of integration with web technologies.

Why this product is good

  • Plotly is considered good because it offers a comprehensive suite of tools for creating interactive visualizations that can be used in web applications, reports, and dashboards. It supports many different types of plots, is easy to use for both beginners and experienced developers, and integrates well with popular programming languages like Python, R, and JavaScript.

Recommended for

    Plotly is recommended for data scientists, analysts, and developers who need to create interactive and visually appealing data visualizations. It's particularly useful for those who work with Python or R and want the ability to embed their visualizations in web applications or dashboards.

vert.x videos

From Zero to Back End in 45 Minutes with Eclipse Vert.x

Plotly videos

Create Real-time Chart with Javascript | Plotly.js Tutorial

More videos:

  • Review - Introducing plotly.py 3.0
  • Review - Is Plotly The Better Matplotlib?
  • Tutorial - Plotly Tutorial 2021
  • Review - Data Visualization as The First and Last Mile of Data Science Plotly Express and Dash | SciPy 2021

Category Popularity

0-100% (relative to vert.x and Plotly)
Web Frameworks
100 100%
0% 0
Data Visualization
0 0%
100% 100
Python Web Framework
100 100%
0% 0
Charting Libraries
0 0%
100% 100

User comments

Share your experience with using vert.x and Plotly. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare vert.x and Plotly

vert.x Reviews

17 Popular Java Frameworks for 2023: Pros, cons, and more
As Vert.x is an event-driven and non-blocking framework, it can handle a lot of concurrencies using only a minimal number of threads. Vert.x is also quite lightweight, with the core framework weighing only about 650 KB. It has a modular architecture that allows you to use only the modules you need so that your app can stay as slick as possible. Vert.x is an ideal choice if...
Source: raygun.com

Plotly Reviews

Best 8 Redash Alternatives in 2023 [In Depth Guide]
Plotly is specifically designed for companies who want to build and deploy analytic applications like dashboards using Python, Julia, or R without needing DevOps or Javascript developers.
Source: www.datapad.io
5 Best Python Libraries For Data Visualization in 2023
Plotly is a web-based data visualization toolkit that comes with unique functionalities such as dendrograms, 3D charts, and also contour plots, which is not very common in other libraries. It has a great API offering scatter plots, line charts, bar charts, error bars, box plots, and other visualizations. Plotly can even be accessed from a Python Notebook.
Top 8 Python Libraries for Data Visualization
Plotly is a free open-source graphing library that can be used to form data visualizations. Plotly (plotly.py) is built on top of the Plotly JavaScript library (plotly.js) and can be used to create web-based data visualizations that can be displayed in Jupyter notebooks or web applications using Dash or saved as individual HTML files. Plotly provides more than 40 unique...
5 top picks for JavaScript chart libraries
Plotly is a graphing library that’s available for various runtime environments, including the browser. It supports many kinds of charts and graphs that we can configure with a variety of options.

Social recommendations and mentions

Plotly might be a bit more popular than vert.x. We know about 34 links to it since March 2021 and only 31 links to vert.x. 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.

vert.x mentions (31)

  • Standing on shoulders: the stack that makes Floci start in ~24ms
    Vert.x is the layer where Floci uses things directly. It's Netty with ergonomics: an event loop, a router, protocol-specific APIs for HTTP, DNS, TCP, WebSockets, gRPC, all sharing the same threading model. - Source: dev.to / 4 months ago
  • What kind of ORM engine does a low-code platform need? (2)
    Traditionally, JDBC interfaces are all synchronous, so JdbcTemplate and HibernateTemplate are also synchronous. But as asynchronous high-concurrency programming spreads, reactive programming has entered mainstream frameworks. Spring now proposes the R2DBC standard, and the vertx framework includes asynchronous connectors for MySQL, PostgreSQL, etc. On the other hand, if an ORM engine acts as a data fusion access... - Source: dev.to / 11 months ago
  • Java News: WildFly 36, Spring Milestones, and Open Liberty Updates
    The sixth release candidate of Eclipse Vert.x 5.0.0 provides support for the Java Platform Module System and a new VerticleBase class. Further details are available in the release notes. - Source: dev.to / over 1 year ago
  • Rust, C++, and Python trends in jobs on Hacker News (February 2025)
    I see your point, but I still don't think you can just say "If you want to get get a job as a Go developer, you must know gRPC." Even more so for Kafka, I've only heard about it being popular in the Java world. You can't even say "If you want to get a job as a Java developer, you must know Spring." Nowadays, sane Java projects use https://vertx.io, it's just too good. I would argue that Spring is for legacy... - Source: Hacker News / over 1 year ago
  • Error handlers and failure handlers in Vert.x
    Vert.x is a toolkit for developing reactive applications on the JVM. I wrote a short introductory post about it earlier, when I used it for a commercial project. I had to revisit a Vert.x-based hobby project a few weeks ago, and I learned that there were some gaps in my knowledge about how Vert.x handles failures and errors. To fill those gaps, I did some experiments, wrote a few tests, and then wrote this blog post. - Source: dev.to / almost 2 years ago
View more

Plotly mentions (34)

  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Let's dive into some practical examples. First, you'll need to set up your environment with the right tools. I recommend using pandas for data manipulation and plotly for visualization. - Source: dev.to / 6 months ago
  • Python for Data Visualization: Best Tools and Practices
    Plotly is perfect for interactive visualizations. You can create interactive charts and graphs that allow users to hover, click, and zoom in. Plotly is also great for web-based visuals, making it easy to share your findings online. - Source: dev.to / over 1 year ago
  • Generative AI Powered QnA & Visualization Chatbot
    Front End: A React application that leverages React-Chatbotify library to easily integrate a chatbot GUI. It also uses the Plotly library to display the charts/visualizations. The generative AI implementation and details are entirely abstracted from the front end. The front-end application depends on a single REST endpoint of the backend application. - Source: dev.to / over 1 year ago
  • Build a Stock Dashboard in less than 40 lines of Python code!🤓
    In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle. - Source: dev.to / almost 2 years ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / about 2 years ago
View more

What are some alternatives?

When comparing vert.x and Plotly, you can also consider the following products

Micronaut Framework - Build modular easily testable microservice & serverless apps

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.

Javalin - Simple REST APIs for Java and Kotlin

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

helidon - Helidon Project, Java libraries crafted for Microservices

Tableau - 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.