Felgo vs. Tableau Desktop

Felgo shines in rapid prototype development and fast iterations. It also has a very short learning curve compared to many other technologies that promise "code once, deploy everywhere." It is not appropriate if you are not willing to mess with the licensing related to Qt but I am still waiting for a clear answer on my doubts from the Felgo team.

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The best scenario is definitely to collect data from several sources and create dedicated dashboards for specific recipients. However, I miss the possibility of explaining these reports in more detail. Sometimes, we order a report, and after half a year, we don't remember the meaning of some data (I know it's our fault as an organization, but the tool could force better practices).

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• Ready available solutions for all platform mobile app development.
• Live hot reloading of UI and other advance controls by Felgo implemented in Qt.
• Using Qt underneath provides an opportunity to developers to use it in all kind of sectors like embedded devices, mobile apps, desktop apps, games etc.

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• An excellent tool for data visualization, it presents information in an appealing visual format—an exceptional platform for storing and analyzing data in any size organization.
• Through interactive parameters, it enables real-time interaction with the user and is easy to learn and get support from the community.

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• QML component / C++ code with hot reload feature.

• Pricing should be more user-friendly and usage-driven
• Making edits to the production reports is fairly tough and has a vast scope of additional capabilities
• Tableau Desktop should be able to differentiate itself from the Tableau server else there is no major meaning of two different products being offered

I am happy with the constant feedback that I am receiving from the team. Although most of it is marketing related, there are some interesting and valuable materials that they have been pushing to ease my learning.

The quality of the documentation and the ease of use may be some important values to take into account.

We believe that the agility that we have acquired until now for developing apps for our business cases give this framework an important lead.

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Our use of Tableau Desktop is still fairly low, and will continue over time. The only real concern is around cost of the licenses, and I have mentioned this to Tableau and fully expect the development of more sensible models for our industry. This will remove any impediment to expansion of our use.

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As with any product, Felgo has some edges to it, but from our past experiences evaluating "code once, deploy everywhere" platforms, we were really surprised at how fast the team went from exploring, into playing and presenting prototypes in very little time.

The dev cycle is really fast as I can get an almost instantaneous feedback on the changes that I make. And it is refreshing to see how I am able to target several platforms at once.

The javascript plus QML combination made it really easy to pick up for me and I am certain sure that many devs can migrate from Web only into multi-platform in a fast manner.

And there is also the possibility to implement C++ code if the need arises to support and bridge native libraries.

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Tableau Desktop has proven to be a lifesaver in many situations. Once we've completed the initial setup, it's simple to use. It has all of the features we need to quickly and efficiently synthesize our data. Tableau Desktop has advanced capabilities to improve our company's data structure and enable self-service for our employees.

When used as a stand-alone tool, Tableau Desktop has unlimited uptime, which is always nice. When used in conjunction with Tableau Server, this tool has as much uptime as your server admins are willing to give it. All in all, I've never had an issue with Tableau's availability.

Tableau Desktop's performance is solid. You can really dig into a large dataset in the form of a spreadsheet, and it exhibits similarly good performance when accessing a moderately sized Oracle database. I noticed that with Tableau Desktop 9.3, the performance using a spreadsheet started to slow around 75K rows by about 60 columns. This was easily remedied by creating an extract and pushing it to Tableau Server, where performance went to lightning fast

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Tableau support has been extremely responsive and willing to help with all of our requests. They have assisted with creating advanced analysis and many different types of custom icons, data formatting, formulas, and actions embedded into graphs. Tableau offers a weekly presentation of features and assists with internal company projects.

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It is admittedly hard to train a group of people with disparate levels of ability coming in, but the software is so easy to use that this is not a huge problem; anyone who can follow simple instructions can catch up pretty quickly.

I think the training was good overall, but it was maybe stating the obvious things that a tech savvy young engineer would be able to pick up themselves too. However, the example work books were good and Tableau web community has helped me with many problems

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Again, training is the key and the company provides a lot of example videos that will help users discover use cases that will greatly assist their creation of original visualizations. As with any new software tool, productivity will decline for a period. In the case of Tableau, the decline period is short and the later gains are well worth it.

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The helpful, responsive customer support. In contrast to frameworks like Flutter, you can count on a professional team that can help you out if needed

I have used Power BI as well, the pricing is better, and also training costs or certifications are not that high. Since there is python integration in Power BI where I can use data cleaning and visualizing libraries and also some machine learning models. I can import my python scripts and create a visualization on processed data.

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Tableau Desktop's scaleability is really limited to the scale of your back-end data systems. If you want to pull down an extract and work quickly in-memory, in my application it scaled to a few tens of millions of rows using the in-memory engine. But it's really only limited by your back-end data store if you have or are willing to invest in an optimized SQL store or purpose-built query engine like Veritca or Netezza or something similar.

• product not released so not applicable.

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• Tableau was acquired years ago, and has provided good value with the content created.
• Ongoing maintenance costs for the platform, both to maintain desktop and server licensing has made the continuing value questionable when compared to other offerings in the marketplace.
• Users have largely been satisfied with the content, but not with the overall performance. This is due to a combination of factors including the performance of the Tableau engines as well as development deficiencies.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info