Software Alternatives, Accelerators & Startups

Dash by Plotly VS Selenium in AWS Lambda

Compare Dash by Plotly VS Selenium in AWS Lambda and see what are their differences

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Dash by Plotly logo Dash by Plotly

Dash is a Python framework for building analytical web applications. No JavaScript required.

Selenium in AWS Lambda logo Selenium in AWS Lambda

Scale Selenium to infinity on demand using our serverless tools. Integrates with your AWS account.
  • Dash by Plotly Landing page
    Landing page //
    2023-05-22
  • Selenium in AWS Lambda Landing page
    Landing page //
    2021-07-13

Dash by Plotly features and specs

  • Interactive Visualizations
    Dash by Plotly allows users to create highly interactive visualizations with ease, using a combination of Python, R, or Julia. It supports a wide variety of visualization components, which can be easily customized and stylized to the user's needs.
  • End-to-End Platform
    Dash is an end-to-end platform that covers the entire data visualization pipeline from data processing to the presentation layer. This allows users to seamlessly transition from data analysis to sharing insights without having to switch tools.
  • Open-Source
    Dash is an open-source framework, which allows for a high level of customization. It benefits from community contributions and offers transparency because users can view and modify the source code as needed.
  • Python Integration
    Dash is tightly integrated with Python, which is a major advantage for data scientists and analysts who use Python for data manipulation and analysis. It leverages the robust ecosystem of Python libraries, like Pandas and NumPy.

Possible disadvantages of Dash by Plotly

  • Limited Custom Components
    While Dash provides many components for building applications, it can sometimes be limiting when you need highly customized features or specific integrations that aren't available out of the box.
  • Learning Curve
    For users not familiar with web development concepts (like HTML, CSS, and JavaScript), Dash can have a steep learning curve because it requires understanding how web applications are structured and deployed.
  • Performance
    Dash applications can become sluggish with large datasets or highly interactive charts, as the client-side rendering can be resource-intensive. This can make it difficult to handle applications at scale without optimization.
  • Deployment Complexity
    Deploying Dash applications might be challenging, especially for users without experience in setting up servers or cloud environments. While there are services provided by Plotly for deployment, they can add extra cost and require technical setup.

Selenium in AWS Lambda features and specs

  • Scalability
    AWS Lambda automatically scales your Selenium tests by running multiple instances simultaneously, allowing for efficient parallel testing without managing servers.
  • Cost-effectiveness
    With AWS Lambda, you only pay for the compute time that you consume, which can significantly reduce costs compared to traditional server-based deployments, especially for occasional testing.
  • Maintenance-free
    AWS Lambda abstracts away server maintenance, updates, and patch management, allowing you to focus exclusively on writing and executing Selenium tests.
  • Integration with AWS Services
    AWS Lambda integrates seamlessly with other AWS services such as S3, DynamoDB, and API Gateway, enabling you to build comprehensive, cloud-native testing workflows.

Possible disadvantages of Selenium in AWS Lambda

  • Execution Time Limitations
    AWS Lambda imposes a maximum execution time limit (15 minutes as of 2023), which may not be sufficient for running extensive Selenium test suites.
  • Cold Start Latency
    When Lambda functions are not frequently invoked, they can experience latency during cold starts, potentially affecting the performance of Selenium tests.
  • Browser Environment Setup
    Running Selenium in AWS Lambda requires setting up browser binaries in a serverless environment, which can be complex and may require custom Lambda layers or container images.
  • Resource Limitations
    Lambda functions have restricted memory and computing capabilities, which might limit the execution of resource-intensive Selenium tests.

Analysis of Selenium in AWS Lambda

Overall verdict

  • Selenium.cloud offers a convenient way to run Selenium-based browser automation on AWS Lambda, providing a serverless, cost-effective, and scalable solution for teams that need occasional or bursty web scraping and testing capabilities without managing dedicated infrastructure.

Why this product is good

  • Serverless architecture eliminates the need to provision or maintain servers for running browser automation
  • Pay-per-use pricing model can significantly reduce costs for intermittent or low-volume automation tasks
  • Automatic scaling handles concurrent execution spikes without manual intervention
  • Simplifies deployment of Selenium scripts by packaging Chrome/Chromium binaries compatible with Lambda's environment
  • Reduces DevOps overhead compared to maintaining Selenium Grid or dedicated VM-based testing infrastructure
  • Integrates well with other AWS services like S3, CloudWatch, and API Gateway for building complete automation pipelines

Recommended for

  • Teams running periodic or scheduled web scraping jobs
  • QA teams needing occasional automated browser testing without maintaining persistent infrastructure
  • Startups and small teams looking to minimize infrastructure costs for browser automation
  • Developers building serverless web scraping or monitoring tools
  • Projects with unpredictable or bursty automation workloads that benefit from auto-scaling
  • Users already invested in the AWS ecosystem seeking tighter integration with existing services

Category Popularity

0-100% (relative to Dash by Plotly and Selenium in AWS Lambda)
Developer Tools
100 100%
0% 0
Web Automation
0 0%
100% 100
Web Frameworks
100 100%
0% 0
Selenium
0 0%
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 Dash by Plotly and Selenium in AWS Lambda

Dash by Plotly Reviews

Top 10 Tableau Open Source Alternatives: A Comprehensive List
To learn more about Plotly-Dash, you can click here to check out their official website.
Source: hevodata.com

Selenium in AWS Lambda Reviews

We have no reviews of Selenium in AWS Lambda yet.
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Social recommendations and mentions

Based on our record, Dash by Plotly seems to be more popular. It has been mentiond 2 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.

Dash by Plotly mentions (2)

  • Other Visualization Tools: Dashboards & Reports
    Cloud Deployment: Dash apps can be deployed to Dash Enterprise or Heroku. - Source: dev.to / 11 months ago
  • [Python] NiceGUI: Lassen Sie jeden Browser das Frontend fรผr Ihren Python-Code sein
    Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who donโ€™t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally,... Source: over 3 years ago

Selenium in AWS Lambda mentions (0)

We have not tracked any mentions of Selenium in AWS Lambda yet. Tracking of Selenium in AWS Lambda recommendations started around Jul 2021.

What are some alternatives?

When comparing Dash by Plotly and Selenium in AWS Lambda, you can also consider the following products

Streamlit - Turn python scripts into beautiful ML tools

Shiny - Shiny is an R package that makes it easy to build interactive web apps straight from R.

Panel - High-level app and dashboarding solution for Python

Bokeh - Bokeh visualization library, documentation site.

Voilร  - Voilร  turns Jupyter notebooks into standalone web applications.

Mercury framework - Mercury allows you to add interactive widgets in Python notebooks, so you can share notebooks as web applications.