Software Alternatives, Accelerators & Startups
Register | Login

Tableau VS PyTorch

Compare Tableau VS PyTorch and see what are their differences

TigerEye
GTM Analytics for the AI Era featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Tableau logo 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.

PyTorch logo PyTorch

Open source deep learning platform that provides a seamless path from research prototyping to...
  • Tableau Landing page
    Landing page //
    2023-10-18
  • PyTorch Landing page
    Landing page //
    2023-07-15

Tableau features and specs

  • User-Friendly Interface
    Tableau offers an intuitive drag-and-drop interface that allows users to create visualizations and dashboards easily, even without extensive technical knowledge.
  • Data Connectivity
    Tableau supports a wide range of data sources including databases, spreadsheets, cloud services, and more, allowing for flexible data integration.
  • Advanced Analytics
    Advanced analytical capabilities, including real-time analytics, trend analysis, and predictive analytics, help users gain deeper insights from their data.
  • Community and Support
    A large, active user community provides a wealth of resources including forums, tutorials, and user groups for support and knowledge sharing.
  • Visualization Quality
    Tableau offers high-quality visualizations with customizable options that make it easier to create compelling reports and dashboards.

Possible disadvantages of Tableau

  • Cost
    Tableau can be expensive, especially for small businesses or individual users, with its various licensing and subscription fees.
  • Performance Issues
    For very large datasets or complex calculations, Tableau can experience performance slowdowns, affecting the efficiency and user experience.
  • Steep Learning Curve for Advanced Features
    While basic features are easy to use, mastering advanced functionalities can require a significant learning curve and technical expertise.
  • Customization Limitations
    Although Tableau is highly customizable, some users find it lacks flexibility when it comes to very specific or unique customization requirements.
  • Export Limitations
    Exporting visualizations and dashboards to formats like PDF or PowerPoint can sometimes be restrictive, limiting the ways reports are shared.

PyTorch features and specs

  • Dynamic Computation Graph
    PyTorch uses a dynamic computation graph, which allows for interactive and flexible model building. This is particularly beneficial for researchers who need to modify the network architecture on-the-fly.
  • Pythonic Nature
    PyTorch is designed to be deeply integrated with Python, making it very intuitive for Python developers. The framework feels more 'native' to Python, which improves the ease of learning and use.
  • Strong Community Support
    PyTorch has a large, active, and growing community. This means abundant resources such as tutorials, forums, and third-party tools are available to help developers solve problems and share solutions.
  • Flexibility and Control
    PyTorch offers granular control over computations and provides extensive debugging capabilities. This level of control is beneficial for tasks that require precise tuning and custom implementations.
  • Support for GPU Acceleration
    PyTorch offers seamless integration with GPU hardware, which significantly accelerates the computation process. This makes it highly efficient for deep learning tasks.
  • Rich Ecosystem
    PyTorch has a rich ecosystem including libraries like torchvision, torchaudio, and torchtext, which are specialized for different data types and can significantly shorten development times.

Possible disadvantages of PyTorch

  • Limited Production Deployment Tools
    PyTorch is primarily designed for research rather than production. While deployment tools like TorchServe exist, they are not as mature or integrated as solutions offered by other frameworks like TensorFlow.
  • Lesser Adoption in Industry
    While PyTorch is popular among researchers, it has historically seen less adoption in industry compared to TensorFlow, which means there might be fewer resources for large-scale production deployments.
  • Inconsistent API Changes
    As PyTorch continues to evolve rapidly, occasionally there are breaking changes or inconsistent API updates. This can create maintenance challenges for existing codebases.
  • Steeper Learning Curve for Beginners
    Despite its Pythonic design, PyTorch's focus on flexibility and control can make it slightly harder for beginners to get started compared to some other high-level libraries and frameworks.
  • Less Mature Documentation
    Although the documentation is improving, it has been historically less comprehensive and mature compared to other frameworks like TensorFlow, which can make it difficult to find detailed, clear information.

Tableau videos

+ Add

Power BI vs Tableau 🔥 5 Factors to Choose a Winner

More videos:

  • Review - What is Tableau Desktop? | A Tableau Desktop Overview
  • Demo - Tableau Software Demo

PyTorch videos

+ Add

PyTorch in 5 Minutes

More videos:

  • Review - Jeremy Howard: Deep Learning Frameworks - TensorFlow, PyTorch, fast.ai | AI Podcast Clips
  • Review - PyTorch at Tesla - Andrej Karpathy, Tesla

Category Popularity

0-100% (relative to Tableau and PyTorch)

Tableau

PyTorch

Data Dashboard
100 100%
Data Dashboard
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Visualization
100 100%
Data Visualization
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Tableau and PyTorch. 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 Tableau and PyTorch

Tableau Reviews

Explore 7 Tableau Alternatives for Data Visualization and Analysis
Welcome to our complete reference, Tableau Alternatives for Data Visualization and Analysis. In this fast-changing digital age, data visualization and analysis have become critical for making informed decisions and strategies. Tableau is a well-known product that has had a considerable impact in this sector. Its user-friendly interface and powerful capabilities have made it...
Source: www.draxlr.com
Explore 6 Metabase Alternatives for Data Visualization and Analysis
To find the best Metabase alternative for your business, start by listing your specific requirements, such as customer support, data integrations, visualization options, user access controls, and budget. Compare these needs with the features of other BI tools like Draxlr, Tableau, Power BI, Looker, or Holistics. Once you've identified a few suitable options, take advantage...
Source: www.draxlr.com
5 best Looker alternatives
Tableau: Tableau is the earliest BI tools built to solve data problems, which means it has a lot of community support for all your queries and can lack what the new-age tools have and are building.
Source: www.draxlr.com
10 Best Alternatives to Looker in 2024
Tableau: Renowned for its powerful visualization capabilities, Tableau enables users to create highly intuitive and interactive dashboards. Favored for its user-friendly interface, Tableau effectively handles large datasets, making it a top choice for data visualization.
Source: www.sigmacomputing.com
6 Best Looker alternatives
Pricing: Looker is typically more expensive than Tableau – but if you’re a growing company with increasing user numbers, Tableau can also get pricey.
Source: trevor.io

PyTorch Reviews

10 Python Libraries for Computer Vision
Similar to TensorFlow and Keras, PyTorch and torchvision offer powerful tools for computer vision tasks. PyTorch’s dynamic computation graph and torchvision’s datasets and pre-trained models make it easy to implement tasks such as image classification, object detection, and style transfer.
Source: clouddevs.com
25 Python Frameworks to Master
Along with TensorFlow, PyTorch (developed by Facebook’s AI research group) is one of the most used tools for building deep learning models. It can be used for a variety of tasks such as computer vision, natural language processing, and generative models.
Source: kinsta.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
PyTorch is another open-source machine learning framework that is widely used in academia and industry. PyTorch provides excellent support for building deep learning models, and it has several pre-trained models for computer vision tasks, making it the ideal tool for several computer vision applications. PyTorch offers a user-friendly interface that makes it easier for...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
When we compare HuggingFace model availability for PyTorch vs TensorFlow, the results are staggering. Below we see a chart of the total number of models available on HuggingFace that are either PyTorch or TensorFlow exclusive, or available for both frameworks. As we can see, the number of models available for use exclusively in PyTorch absolutely blows the competition out of...
Source: www.assemblyai.com
15 data science tools to consider using in 2021
First released publicly in 2017, PyTorch uses arraylike tensors to encode model inputs, outputs and parameters. Its tensors are similar to the multidimensional arrays supported by NumPy, another Python library for scientific computing, but PyTorch adds built-in support for running models on GPUs. NumPy arrays can be converted into tensors for processing in PyTorch, and vice...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, PyTorch seems to be a lot more popular than Tableau. While we know about 132 links to PyTorch, we've tracked only 8 mentions of Tableau. 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.

Tableau mentions (8)

  • Tableau Certified Data Analyst Exam Readiness
    Hey everyone, I'm interested in taking the Tableau Certified Data Analyst Exam Readiness course through tableau.com to prepare and get Tableau certified. I had some questions about the course, such as are the videos pre recorded or in person, do you have access to the material once the 90 days expire, and I was also wondering if anyone had input/advice for this course. Thanks! Source: almost 2 years ago
  • Where to publish knowledge sharing on Tableau reverse engineering and data dictionary generation?
    Could anyone recommend what media I should approach to publish my work (internet or print). I could try the Tableau forum in tableau.com but it's not very active + Tableau may be unappreciative as my work overlaps with their (pricey) data management solution. Plus it needs to be some high visibility / reputable media to count for my career development. Any recommendations welcome thanks!!! Source: over 2 years ago
  • I have huge loads of data in Redshift. How can I make this available to end-users after performing few procs and queries? It should be available online.
    Tableau public: tableau.com. Big player but your data will be made public and not really user-friendly data model. Source: about 3 years ago
  • What tips do you have on evaluating various BI tools for business needs? What are the essential criteria's you would include when evaluating different tools? The goal is to have an unbiased, objective approach.
    For example, we have a project to compare Tableau, Power BI, and InetSoft. The need for strong pagination-based email delivery eliminated Tableau. AWS's Linux instance is the targeted platform which makes Power BI less than ideal. Source: about 3 years ago
  • Anyone go into Data Analytics after this program?
    I just started learning Tableau because our dept is transitioning into Tableau from Power BI. Since I already have years of experience with Power BI I just went over their tutorials from tableau.com and got onboarded pretty quick. I'm still learning it but I'm at least able to build out reports and get things done. Its not too difficult to pickup one BI tool when you have experience with another. Source: about 3 years ago
View more

PyTorch mentions (132)

  • Top Programming Languages for AI Development in 2025
    With the quick emergence of new frameworks, libraries, and tools, the area of artificial intelligence is always changing. Programming language selection. We're not only discussing current trends; we're also anticipating what AI will require in 2025 and beyond. - Source: dev.to / 8 days ago
  • Fine-tuning LLMs locally: A step-by-step guide
    Next, we define a training loop that uses our prepared data and optimizes the weights of the model. Here's an example using PyTorch:. - Source: dev.to / 29 days ago
  • 10 Must-Have AI Tools to Supercharge Your Software Development
    8. TensorFlow and PyTorch: These frameworks support AI and machine learning integrations, allowing developers to build and deploy intelligent models and workflows. TensorFlow is widely used for deep learning applications, offering pre-trained models and extensive documentation. PyTorch provides flexibility and ease of use, making it ideal for research and experimentation. Both frameworks support neural network... - Source: dev.to / 3 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    Frameworks like TensorFlow and PyTorch can help you build and train models for various tasks, such as risk scoring, anomaly detection, and pattern recognition. - Source: dev.to / 3 months ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
View more

What are some alternatives?

When comparing Tableau and PyTorch, you can also consider the following products

Microsoft Power BI - BI visualization and reporting for desktop, web or mobile

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Sisense - The BI & Dashboard Software to handle multiple, large data sets.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Loading...