Tableau Desktop is a data visualization product from Tableau. It connects to a variety of data sources for combining disparate data sources without coding. It provides tools for discovering patterns and insights, data calculations, forecasts, and statistical summaries and visual storytelling.
Compared to vis.js and d3, Gephi has a much better UI and is easier to use for anyone without a strong code background. vis.js and d3 are more flexible in terms of output and are used by Quaero for more of the ongoing reporting where Gephi is used for exploratory analysis and …
It is well suited for projects that are more discovery related. If this is a one-time project that we create a visual for, this would definitely make sense to use. If this is an ongoing analysis (monthly for example), we might look to another software that we would be able to automate a little further in how the visualization comes together
Tableau Desktop is one the finest tool available in the market with such a wide range of capabilities in its suite that makes it easy to generate insights. Further, if optimally designed, then its reports are fairly simple to understand, yet capable enough to make changes at the required levels. One can create a variety of visualizations as required by the business or the clients. The data pipelines in the backend are very robust. The tableau desktop also provides options to develop the reports in developer mode, which is one of the finest features to embed and execute even the most complex possible logic. It's easier to operate, simple to navigate, and fluent to understand by the users.
In comparison to other tools such as GraphWiz or Circos, Gephi comes with an intuitive, easy-to-use interface that makes it easy to load your data, and quickly start building all sorts of different graphs. There's absolutely no code that needs to be written for either loading or modeling. And without downloading additional plug-ins, Gephi ships with quite a few standard graph models, as well as some "fun" extras such as the Sierpinski triangle, and a variety of force atlas types.
Most of the layout types (maybe all) are highly configurable, which can make for extremely customized and unique displays of your data. Again, none of this requires the user to write any code. That said, it is possible to script custom functionality for your models, or even update the Java source code yourself, if you feel like getting technical. Gephi builds are available on GitHub, and the developers encourage people to contribute ideas, improvements, and plug-ins.
There's a plug-in for Gephi that allows for streaming data to update your model. This essentially allows you to create near realtime graphs of your data in motion. This plug-in was by far the biggest reaston we invested time in the product; to create animated data visualizations without exhaustive hours in development.
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.
I (and many others) have had to expand Gephi's memory manually by experimenting with the configuration file. I'm glad it's possible, but it should be easier.
Gephi sometimes crashes inexplicably and loses your work, so I have developed a habit of explicitly exporting versions of my graphs as csv's, but I think this should be handled automatically in Gephi.
Because it is prone to crash, ideally, Gephi would help the user manage his/her use, by estimating processing and memory for very large tasks and prompting the user to confirm their requests before executing. Instead, I just tend to avoid certain functions.
While Gephi isn't perfect, it's a powerful tool for mathematical graph modelling that's hard to find in other products, particularly by way of its interface. It grants non-software developers access to a point-and-click way of creating accurate, beautiful visualizations that would normally take hours in other applications. The fact that it allows for live streaming data is also something that's hard to come by, at least for visualization software
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.
Gephi is very intuitive and the fact that it shows its process helps the user understand what's going on. However, the animation features can really slow it down and there isn't a way to shut them off. Furthermore, the failures on saving mean you frequently have to start over. These problems disrupt the workflow and can be frustrating.
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
I have never really used support much, to be honest. I think the support is not as user-friendly to search and use it. I did have an encounter with them once and it required a bit of going back and forth for licensing before reaching a resolution. They did solve my issue though
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.
The training for new users are quite good because it covers topic wise training and the best part was that it also had video tutorials which are very helpful
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.
The interactivity in Gephi and the quality of the output figures are impressive. However, the selling point was the fact that we were able to link Gephi into our pipeline using Java's interface. Other products were less customizable and lacking of the sophistication Gephi provided without too much pain during the liking process.
If we do not have legacy tools which have already been set up, I would switch the visualization method to open source software via PyCharm, Atom, and Visual Studio IDE. These IDEs cannot directly help you to visualize the data but you can use many python packages to do so through these IDEs.
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.
I have only used the product for education purposes. I will not be the best person to provide details about ROI and business efficiency and customer service. I was personally very excited about the tool and am continuing my work on the tool.
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.
