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
SAS Viya
Score 6.8 out of 10
N/A
An end-to-end platform for AI, data science, and analytics, used for modeling, as well as management and deployment of AI models.
N/A
Pricing
KNIME Analytics Platform
SAS Viya
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
No answers on this topic
Offerings
Pricing Offerings
KNIME Analytics Platform
SAS Viya
Free Trial
No
Yes
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
KNIME Analytics Platform
SAS Viya
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
SAS is just as good as these tools but is pricier. I like that it handles data visualization and modeling together in one platform that's a novel mechanism that is fairly rare. Also, it's forecasting capabilities are nicely integrated with the functionality overall which makes …
We had major use of SAS in forecasting where it doesn't require high level of coding knowledge and which has highly efficient models built in which can give good results on forecasts without lot of manual intervention. This tool was designed specifically for forecasting and …
SAS is faster then both SPSS and STATA. SAS also has better models and graphs when comparing the three softwares. However, STATA and SPSS are more user friendly. It is easy to use SPSS and STATA, because a lot of it is point-click. SAS requires some training to be able to use …
Director, Application Architecture and Programming
Chose SAS Viya
SAS has a much superior and comprehensive data preparation capability with a clear approach on how to handle and scale for a large amount of data and users. However, it can be more expensive to implement.
R is of course much cheaper (free) than SAS Analytics, and it can do everything SAS Analytics can do and more. It is a much more technical tool than SAS Analytics, which is why some people prefer SAS Analytics.
SAS was the incumbent tool, and what the team knew. We did look into using Revolution Analytics enterprise version of R, but the learning curve on that caused us to stick with SAS. In my current position, I've opted for WPS over SAS. I can still leverage my SAS experience, but …
SAS allows the user a wider range of capabilities to cleanse and manipulate the data. Not only can the data be pulled directly into SAS, but before it is finalized it can be transposed, graphed, or altered in any way imaginable which puts it a step above the Business Objects …
Features
KNIME Analytics Platform
SAS Viya
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
KNIME Analytics Platform
9.2
Ratings
10% above category average
SAS Viya
-
Ratings
Connect to Multiple Data Sources
9.60 Ratings
00 Ratings
Extend Existing Data Sources
10.00 Ratings
00 Ratings
Automatic Data Format Detection
9.10 Ratings
00 Ratings
MDM Integration
7.90 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
KNIME Analytics Platform
8.1
Ratings
3% below category average
SAS Viya
-
Ratings
Visualization
8.00 Ratings
00 Ratings
Interactive Data Analysis
8.10 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
KNIME Analytics Platform
8.3
Ratings
2% above category average
SAS Viya
-
Ratings
Interactive Data Cleaning and Enrichment
9.00 Ratings
00 Ratings
Data Transformations
9.50 Ratings
00 Ratings
Data Encryption
7.40 Ratings
00 Ratings
Built-in Processors
7.40 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
KNIME Analytics Platform
8.0
Ratings
5% below category average
SAS Viya
-
Ratings
Multiple Model Development Languages and Tools
9.50 Ratings
00 Ratings
Automated Machine Learning
8.20 Ratings
00 Ratings
Single platform for multiple model development
9.30 Ratings
00 Ratings
Self-Service Model Delivery
5.00 Ratings
00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
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.
We piloted SAS AA at my organization to see how well it compares with other free software tools such as RStudio and Anaconda. So far what we saw was very impressive especially with the visual display but was a little out of our price range. It would be useful in analyzing population health metrics combined with financial data.
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
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.
SAS Analytics does not have very good graphic capabilities. Their advanced graphics packages are expensive, and still not very appealing or intuitive to customize.
SAS Analytics is not as up-to-date when it comes to advanced analytical techniques as R or other open-source analytics packages.
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
Not only does SAS become easier to use as the user gets more familiar with its capabilities, but the customer service is excellent. Any issues with SAS and their technical team is either contacting the user via email, chat, text, WebEx, or phone. They have power users that have years of experience with SAS there to help with any issue.
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.
If SAS Enterprise Guide is utilized any beginning user will be able to shorten the learning curve. This is allow the user a plethora of basic capabilities until they can utilize coding to expand their needs in manipulating and presenting data. SAS is also dedicated to expanding this environment so it is ever growing.
SAS probably has the most market saturation out of all of the analytics software worldwide. They are in every industry and they are knowledgable about every industry. They are always available to take questions, solve issues, and discuss a company's needs. A company that buys SAS software has a dedicated representative that is there for all of their needs.
Although nothing is perfect, SAS is almost there. The software can handle billions of rows of data without a glitch and runs at a quick pace regardless of what the user wants to perform. SAS products are made to handle data so performance is of their utmost important. The software is created to run things as efficiently as SAS software can to maximize performance.
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.
SAS is generally known for good support that's one of the main reasons to justify the cost of having SAS licenses within our organization is knowing that customer support is just a quick phone call away. I've usually had good experiences with the SAS customer support team it's one of the ways in which the company stands out in my view.
SAS has regional and national conferences that are dedicated to expanding users' knowledge of the software and showing them what changes and additions they are making to the software. There are user groups in most of the major cities that also provide multi-day seminars that focus on specific topics for education. If online training isn't the best way for the user, there is ample in-person training available.
There are online videos, live classes, and resource material which makes training very easy to access. However, nothing is circumstantial so applying your training can get tricky if the user is performing complex tasks. When purchasing software, SAS will also allocate education credits so the user(s) can access classes and material online to help expand their knowledge.
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.
Ask as many questions you can before the install to understand the process. Since a third party does the installation your company is sort of a passanger and it is easy to get lost in the process. It also helps to have all users and IT support involved in the install to help increase the knowledge as to how SAS runs and what it needs to perform correctly.
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.
SAS is faster then both SPSS and STATA. SAS also has better models and graphs when comparing the three softwares. However, STATA and SPSS are more user friendly. It is easy to use SPSS and STATA, because a lot of it is point-click. SAS requires some training to be able to use it as effectively as possible. SAS is better with large data sets, and it is easier to analyze many data points at the same time
It all depends on the type of SAS product the user has. Scaleability differs from product to product, and if the user has SAS Office Analytics the scaleability is quite robust. This software will satisfy the majority of the company's analytic needs for years to come. In addition, if SAS is not meeting the users needs the company can easily find SAS solutions that will.
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.