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
per month
SAS Enterprise Guide
Score 9.6 out of 10
N/A
SAS Enterprise Guide is a menu-driven, Windows GUI tool for SAS.
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.
Of the software tools I have tried, SAS Enterprise Guide is my preferred choice because of the efficiency of processing, the line-by-line processing, the ease of writing code, and the customer support. SAS appears to have put lots of effort into making SAS Enterprise Guide one …
KNIME Analytics Platform is excellent for people who are finding Excel frustrating, this can be due to errors creeping in due to manual changes or simply that there are too many calculations which causes the system to slow down and crash. This is especially true for regular reporting where a KNIME Analytics Platform workflow can pull in the most recent data, process it and provide the necessary output in one click. I find KNIME Analytics Platform especially useful when talking with audiences who are intimidated by code. KNIME Analytics Platform allows us to discuss exactly how data is processed and an analysis takes place at an abstracted level where non-technical users are happy to think and communicate which is often essential when they are subject matter experts whom you need for guidance. For experienced programmers KNIME Analytics Platform is a double-edged sword. Often programmers wish to write their own code because they are more efficient working that way and are constrained by having to think and implement work in nodes. However, those constraints forcing development in a "KNIME way" are useful when working in teams and for maintenance compared to some programmers' idiosyncratic styles.
SAS Enterprise Guide is good at taking various datasets and giving analyst/user ability to do some transformations without substantial amounts of code. Once the data is inside SAS, the memory of it is very efficient. Using SAS for data analysis can be helpful. It will give good statistics for you, and it has a robust set of functions that aid analysis.
I think the most useful aspect of SAS Enterprise Guide is the ability to use a point-and-click interface to create graphics, transform data, and perform statistics. The best part is that SAS Enterprise Guide creates base SAS code from the process, making it easy to reproduce analyses.
SAS Enterprise Guide makes creating summary statistics about as easy as it gets. If one doesn't know proc means or proc tabulate, one can use SAS Enterprise Guide instead.
The time-series forecasting procedures within SAS Enterprise Guide produce fairly good results. SAS Enterprise Guide makes time-series model comparisons relatively straight-forward.
SAS Studio has some great examples that can be implemented. Adding a filter to the output datasets for one.
Some issues around having to enter my password every time I open it up. Some people are having this issue and others aren't. SAS admin is at a loss to work out why it's occurring.
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
KNIME Analytics Platform offers a great tradeoff between intuitiveness and simplicity of the user interface and almost limitless flexibility. There are tools that are even easier to adopt by someone new to analytics, but none that would provide the scalability of KNIME when the user skills and application complexity grows
It's not all bad, but I don't believe that an enterprise purchase of SAS is worth the expense considering the widely available set of tools in the data analytics space at the moment. In my company, it's a good tool because others use it. Otherwise, I wouldn't purchase a new set of it because it doesn't have some of the better analytical functions in it.
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.
I did not use the technical support of SAS EG. I can say that I have had hard time to find online tutorials or projects for SAS EG. For instance, it is hard to find completed researches or designed algorithms used with SAS EG. Sometimes it just depends on user's skill set and experience with databases and programming.
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.
I've not worked hands-on with the implementation team, but there were no escalations barring a few hiccups in the deployment due to change in requirement & adoption to our company's remote servers.
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% functionality and UI at no cost would be enough for me to quit using Alteryx and move towards [KNIME Analytics].
Why I prefer SAS EG: Data processing speed is much faster than that R Studio. It can load any amount of data and any type of data like structured or unstructured or semi-structured. Its output delivery system by which we have the output in PDF file makes it very comfortable to use and share that file to clients very easily. Inbuilt functions are very powerful and plentiful. Facility of writing macros makes it far away from its competitors.
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.
Positive (cost): SAS made a bundle that include unlimited usage of SAS/Enterprise Guide with a server solution. That by itself made the company save a lot of money by not having to pay individual licences anymore.
Positive (insight): Data analysts in business units often need to crunch data and they don't have access to ETL tools to do it. Having access to SAS/EG gives them that power.
Positive (time to market): Having the users develop components with SAS/EG allows for easier integration in a production environment (SAS batch job) as no code rework is required.
