Tableau Desktop vs. Tricentis NeoLoad

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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While Tricentis NeoLoad supports SAP GUI protocols, recording and maintaining test scripts for complex SAP transactions (like those in the OTC module) can be cumbersome.


For instance, we had challenges recording a VA01 (Sales Order Creation) transaction because of the dynamic nature of SAP screen fields and session handling. NeoLoad sometimes struggles to properly correlate or handle SAP’s SAPGUI Dynpro fields automatically.

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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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• With Tricentis NeoLoad, we have control on the test dynamically. One can alter user count/volume even when test is in progress. This makes it a very cool tool as you can avoid running multiple tests eventually saves a lot of time.
• Tricentis NeoLoad user paths are very well structured. End points can be identified and tweaked easily especially when a large list of endpoints are part of the neoload project.
• Tricentis NeoLoad results comparison is one of the cool options. We can compare previous tests against the current one to see the exact delta between transaction metrics. This helps in gauging the application behaviour with changes deployed

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• 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

• Ease of use: Some users find certain features or aspects of the interface to be confusing or hard to use. Improving the overall usability of the tool could make it more accessible and easier for users to get up and running with it.
• Integration with other tools: Tricentis NeoLoad does not have many integrations with other tools. Adding more integrations with popular tools and platforms could make it easier for users to work with Tricentis NeoLoad as part of their overall workflow.
• Mobile testing: Some users wish that Tricentis NeoLoad had more robust support for testing the performance of mobile applications. This could include the ability to simulate different mobile devices and networks, as well as the ability to test the performance of native mobile apps.
• Cloud support: Tricentis NeoLoad does not have as much support for cloud-based applications and infrastructure. Adding more support for testing the performance of applications hosted in the cloud could be a useful feature for some users.
• Advanced scripting capabilities: While Tricentis NeoLoad does offer some scripting capabilities, it would be nice to have more advanced options for customizing and automating load testing scenarios. Adding more advanced scripting capabilities could make it easier for users to create complex load testing scenarios.

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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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We find NeoLoad easy to use compared to their competitors. It requires minimal coding knowledge, also it is easy to train newcomers with NeoLoad. It also offers good integration and automation capability where with less code you can automate your testing process

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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.

In being flexible to meet all needs, it is very bare bones by design, but this could be a considered a feature and not a bug.

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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This is the only area where we strongly feel Tricentis needs to improve. Their support process is inadequate, representatives are not so capable of providing solutions at one shot without consulting their internal engineering teams. Most importantly, the time they take to resolve simple issues is huge and at many times hampered our work.

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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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1. Their documentation is scattered over many places like GitHub read me files, their internal website etc;
2. Support team takes huge time just to acknowledge our ticket
3. No support to automatically migrate scripts from competetor tool into

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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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When we used Apache JMeter we faced some problems like less documentation is available for integrating tools, and some integrations have not yet been deployed. Very few plugin updates are made. Since customer support is an open source tool, it is a major problem. While Tricentis Neoload provides better documentations and customer services.

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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.

• 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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• Performance testing is like an insurance. An online store which is not available causes a loss of sales. Without a performance test you do not know if it might happen. The invest into performance tests reduces the risk of loosing sales. The flexible license model of NeoLoad allows or customers to choose reasonable and suiteable rate.

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