Swiss company KNIME offers their KNIME Analytics Platform for big data and predictive analytics.
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Tableau Desktop
Score 8.4 out of 10
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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.
$70
per month
Pricing
KNIME Analytics Platform
Tableau Desktop
Editions & Modules
No answers on this topic
Tableau Creator
$70.00
Per User / Per Month
Offerings
Pricing Offerings
KNIME Analytics Platform
Tableau Desktop
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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All pricing plans are billed annually.
More Pricing Information
Community Pulse
KNIME Analytics Platform
Tableau Desktop
Considered Both Products
KNIME Analytics Platform
Verified User
Engineer
Chose KNIME Analytics Platform
KNIME provides visualisation capabilities and much more when compared to Tableau.
Knime is a more flexible option in some ways, allowing for more data manipulation if you can find the right node. It is not as scaleable in some cases, and some tasks are just easier and faster on SQL databases. It does not build charts or reports as easily as a Tableau and …
[KNIME Analytics] is greatly suited for repetitive tasks one has to perform in excel as it automates these mundane tasks. [KNIME Analytics] is also well suited for creating a seamless connection with other BI tools to enable hands-free file sharing. [KNIME Analytics] has improvements to make on the overall User interface, its data visualization package and advanced level of AI-related tasks such as text mining,
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.
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.
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.
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
Since it is relatively new, there has not developed a vast previously asked/frequently asked questions library that comes up when you google an issue you come across with. This will happen only in time, and as the community grows. Because of the same reason, the community is not big. Consequently, it is possible not to receive good, fast responses to asked questions in community hubs and forums.
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.
Alteryx : allows for generally "data" knowledgeable workers to easily implement and develop a data model in an automated fashion. The collaboration tools built in also make is easy for members to share work, best practices, and custom modules
Alteryx is not cloud based solution where as KNIME Analytics has that
Alteryx was selected due to better user interface and better in data analytics
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.
It is suited for data mining or machine learning work but If we're looking for advanced stat methods such as mixed effects linear/logistics models, that needs to be run through an R node.
Thinking of our peers with an advanced visualization techniques requirement, it is a lagging product.
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.
