KNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services.
$0
Tableau Desktop
Score 8.4 out of 10
N/A
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.
$115
per month (billed annually) per user
Pricing
KNIME Analytics Platform
Tableau Desktop
Editions & Modules
KNIME Community Hub Personal Plan
$0
KNIME Analytics Platform
$0
KNIME Community Hub Team Plan
€99
per month 3 users
KNIME Business Hub
From €35,000
per year
Tableau Creator License
$115
per month (billed annually) per user
Offerings
Pricing Offerings
KNIME Analytics Platform
Tableau Desktop
Free Trial
No
No
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
—
All pricing plans are billed annually. A Creator license includes Tableau Desktop, Tableau Prep Builder, and Tableau Pulse. Discounts sometimes available for volume.
More Pricing Information
Community Pulse
KNIME Analytics Platform
Tableau Desktop
Considered Both Products
KNIME Analytics Platform
Verified User
Anonymous
Chose KNIME Analytics Platform
Alteryx is a very similar product, almost all the things that are achievable in KNIME Analytics Platform can be done in Alteryx as well, but you have to pay for the Desktop version to conduct the analysis. But with KNIME Analytics Platform it is totally free and can be used …
As a commercial product Alteryx is more polished and can be even easier for a beginner, but KNIME beats Alteryx in functionality and performance. Dataiku takes the integration with Python and Git further than KNIME but isn't at the level of Alteryx and KNIME with its No …
There are two aspects which put KNIME Analytics Platform ahead of other products. Firstly the fact that KNIME Analytics Platform comes at no cost and no restrictions on its use is an instant winner for any organisation wanting to democratise their data. It means that a client …
Our organization also reviewed the Alteryx platform. From our experience KNIME had more functionality, was more stable, responsive, had more features, and was overall a better product from our experience. Alteryx is also a paid product, while KNIME is free.
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
Having used both the Alteryx and [KNIME Analytics] I can definitely feel the ease of using the software of alteryx. The [KNIME Analytics] on the other hand isn't that great but is 90% of what alteryx can do along with how much ease it can do. Having said that, the 90% …
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 …
Data Scientist - Biotech Data Science Digtialization (BDSD)
Chose KNIME Analytics Platform
KNIME Analytics Platform has a nice visualization comparing to Azure Machine Learning Studio. KNIME also has a good amount of built-in preprocessing nodes and ML training nodes that makes it easier to develop workflow instead of writing codes. However this also limits the …
KNIME is a lower price point and has strong cross platform capabilities. Other platforms are locked to a specific operating system and cost in some cases substantially more, making them less good choices for smaller businesses that still need basic data unification. The fact …
Comparing the KNIME Analytics Platform to Anaconda and MATLAB, KNIME Analytics Platform's upsides are ease of use thanks to graphical interface and intuitiveness, no requirement of programming/coding and pre-existing nodes. Anybody can use it and create models even though …
We need to use SAS/STAT package within SAS to use the advanced statistical functions, but KNIME has inbuilt libraries for the same. Also, the integration with Open source (Python, R, Java codes) allows better scalability & more availability of skilled resources to work upon.
Knime is much more user simple than any high-level programming language. The ability to connect nodes ad produces outputs in minutes is a large benefit for this program
Both power bi and Tableau Desktop has its own pros and cons. Microsoft power bi is best to work with Microsoft products. however for fast connection with diverse range of integration with data sources Tableau Desktop is best. if you are cost sensitive power bi is best option …
Tableau is more flexible than these - I liked Qlikview old version a lot but have not used the Qlik Sense etc new ones. Tableau user logic is harder to understand than Looker Studio. However it's more trust worthy. Connecting internet sources to Tableau Desktop is much harder. …
Tableau Desktop is older and just better overall. It has more capabilities and is more useful to have. I don't think you could have Alteryx as a standalone product like you can with Tableau Desktop. You'd want another bi tool.
Tableau Desktop has a more easy to use drag and drop interface and is easier to learn. It also allows greater customization of charts than Power BI. However, Tableau Desktop costs more than Power BI which is bundled into our Microsoft contract at no additional charge. Power BI …
The visualizations are far and away more powerful and it is more user friendly than Power BI. It would take 3-4 times as long to create the types of reports in Excel that I can create in Tableau Desktop and there are a slew of ways I can present the data in Tableau Desktop that …
It has a better user interface compared to Microsoft Power BI. The Tableau integration process is quite simple and clear with the third-party application whereas Power BI is not easily integrated with other tools and requires a complex process to follow for integration. DAX …
When it comes to pricing, Tableau is kinda expensive but worth it as it has more features, not just features but really useful features that make our work easier especially as a project manager I need to pull up data almost every day in our meetings, and I find Tableau useful …
Tableau can create visually attractive customizable dashboards than can quickly by drag-drop while in power bi we can create simple dashboard. Power bi support lesser data source while in Tableau there is a lot of options When we talk about data handling tableau is a clear …
Tableau Desktop is clearly one of the best in the business. It has incredible capabilities, and many features are extremely useful. The intuitiveness of the dashboards and the graphical nature of the visualizations are widely used features and super helpful. One of the other …
Tableau Desktop provides some state of the art feature and capabilities that are just awesome. Its support, online blog, and tutorials are better than its competitors. That was the best selling point for me.
With Tableau Desktop, it's easy to create a report in the
context quickly. It allows for the seamless management of the data sources,
which is convenient for the data users. Because it is simple to use, it is
It does have a lot of potential when using Microsoft other technologies - in integration/Embedded, Visuals and connectivity to data sources. Advanced analytics is also smooth when working on python/r scripts. Automated insights are better in Tableau/Alphaa AI. NLG/NLQ - …
For complex data visualization, Tableau Desktop shines. Even though it uses highly granular databases, it has a powerful engine that can process large amounts of data quickly and produce high-quality charts. It has the broadest range of APIs and is extremely simple. The …
We decided to use Tableau Desktop as that's fairly standard in the industry, it is being taught in college, and is widely known. Tableau Desktop is nice, but in my opinion, it is VERY expensive. Unless you are really making money off of decisions, then your ROI is going to be …
Using Tableau Desktop, we have found it the most actionable and user-friendly application ever. It has the broadest range of APIs and is exceptionally user-friendly. It can handle a large amount of data and produce smooth charts quickly. For data geeks, this is the ideal stack.
When compared to Power BI, Tableau has a more flexible deployment. You can install the desktop version without having to install the SQL server. Tableau got you covered end-to-end — from collaboration, analytics, content discovery, data prep & access, down to deployment. …
Tableau Desktop is preferred over other BI software because it allows for more data visualization, storytelling, and dashboards. Microsoft Power BI may be a better option if you need to perform data modeling, however. Tableau Desktop is an excellent tool for nearly all other …
We preferred Tableau over Power BI due to its user-friendly interface and interactive GUI. Since we work with large datasets, we observed that Power BI can deal with only a limited amount of data when compared to Tableau which creates complex visualizations in a time-efficient …
Tableau Desktop is the most user-friendly and actionable application we have used in comparison to others. It has the best API connection potential along with easy start-up. They seem to always be updating the platform to solve newer problems which help keep my company up to …
We also use Power BI for small projects and teams that can't afford to pay for Tableau licenses. Tableau has more features and is more robust compared to Power BI. They also provide better and faster support compared to Microsoft. It is the standard visualization tool, but …
KNIME Analytics Platform has vastly improved our effectiveness when working with large data sets. The self documenting GUI allows analysts to focus on what they are trying to accomplish, not complex code syntax. If we were to use traditional tools, like SQL, work would take much longer and it would be more difficult to collaborate both internally and with clients. Since KNIME Analytics Platform is database oriented, some spreadsheet functions are not supported, which is as it should be. For small data sets we often use Excel vlookup and pivot tables in place of KNIME Analytics Platform. If VBA code is requried, we go to KNIME Analytics Platform as we find VBA to be unstable in Excel.
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).
Visual programming as oppose to scripting encourages data analysts to reap deeper insights from their data
Large community contribution in extending the KNIME Analytics Platform into other areas of analytics, e.g. Text Analytics, Predictive Analytics, ML, etc.
Open source with periodic updates ensures it is equipped to deal with the most sophisticated data analytics use case
The Visualizations graphics are really good and the color options help in designing attractive charts. They help to convey more information and can be made interactive.
You can add filters with offer you to plug and play with values and understand different outcomes.
You can drag and drop options while creating charts and dashboards. also it is a very fluid layout.
Automation - e.g. RapidMiner Studio provides a Turbo Prep function, where one can get to working on models more quickly (RapidMiner is not open source though)
KNIME does not replace a regular reporting tool - it is not meant to. However, if I have already spent some time developing a data acquisition and analytical model, it would be nice to be able to deploy, for example, a monitoring or reporting module that would process data autonomously and react accordingly.
We are happy with Knime product and their support. Knime AP is versatile product and even can execute Python scripts if needed. It also supports R execution as well; however, it is not being used at our end
Because right now its the best option out there (disclosure: I haven't used Qlikview or some of the other direct competitors of Tableau). The big investment is in Tableau Server not desktop. For the cost of the license of Tableau desktop, its a pretty good deal. You can hook it up to pretty much any data source easily. You can easily share the visualizations with your team/colleagues easily. Tableau Desktop is generally easy to use for business users. But the more advanced stuff is better suited for a analyst or someone with a IT/CS background.
The training KNIME Analytics Platform provide helps you get to grips with a product that is already very intuitive. There is a KNIME Analytics Platform way of thinking about addressing problems, but once you understand a couple of patterns which you see again and again in your workflow it all makes sense.
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
KNIME's HQ is in Europe, which makes it hard for US companies to get customer service in time and on time. Their customer service also takes on average 1 to 2 weeks to follow up with your request. KNIME's documentation is also helpful but it does not provide you all the answers you need some of the time.
The Tableau Desktop's support team has been very helpful and tend to response very quickly. After all you have paid very premium price for the product and it goes to the services. This makes using the tool much easier for these who doesn't have such experience to get help quickly.
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
KNIME Analytics Platform is easy to install on any Windows, Mac or Linux machine. The KNIME Server product that is currently being replaced by the KNIME Business Hub comes as multiple layers of software and it took us some time to set up the system right for stability. This was made harder by KNIME staff's deeper expertise in setting up the Server in Linux rather than Windows environment. The KNIME Business Hub promises to have a simpler architecture, although currently there is no visibility of a Windows version of the product.
Time needs to be spent ahead of implementation to make sure data sources are set up and ready. Consultants need to understand the data sources and the goals before setting foot on-site. Installation is easy, learning to use it takes time. The training resources available are great.
There are two aspects which put KNIME Analytics Platform ahead of other products. Firstly the fact that KNIME Analytics Platform comes at no cost and no restrictions on its use is an instant winner for any organisation wanting to democratise their data. It means that a client is free to install it on as many machines as they wish without worrying about costs, the number of seats required or payment models or procurement negotiation. It also means that we are not building costs into our clients business. Secondly, KNIME Analytics Platform has a very comprehensive set of tools for importing/exporting data, data manipulation and data science. Some products offer analytics packages on top of their base offering at additional cost and they are still not as comprehensive as what you get with KNIME Analytics Platform for free. For some types of analysis you may require to download additional packages with KNIME Analytics Platform, but its invariably at no cost, those packages are kept out of the main download to keep the size down. Due to the easy integration with R and Python, I view KNIME Analytics Platform as also having the capabilities of those languages too. This has helped me in the past with seamlessly importing a rare filetype and using very specific models not directly available in KNIME Analytics Platform.
Tableau Desktop is clearly one of the best in the business. It has incredible capabilities, and many features are extremely useful. The intuitiveness of the dashboards and the graphical nature of the visualizations are widely used features and super helpful. One of the other benefits is that both programmers and non-programmers can equally explore and create their own opportunities, and seamless integration is possible.
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.