Alteryx alternatives and competitors

We primarily use the product for data analysis and investigations.  

I think that the ease of use is the most valuable feature. I know SQL. I even taught SQL. But new people who come to the tool without in-depth knowledge of SQL or people who do not have an analytics background can still use the tool. It is very easy for these new users to climb the learning curve using SAS Enterprise Guide because of the way it was created with ease-of-use in mind. It is well-oriented to the business user.  

In no time new users are able to create workflows. Obviously, it is best if they have some basic knowledge of how things work in doing analysis — like the concept of joins, data profiling, and maybe some other SQL-related concepts. But the key here is they do not need to know how it is coded or the exact commands. All the features are available within Enterprise Guide through a drag-and-drop interface so queries can be built without extensive knowledge of how that works behind the scenes.  

The ease-of-use makes the tool valuable even for someone who is near the beginner level.  

I think machine learning should be added to the product. It already has virtually everything from the data wrangling perspective. Machine learning concepts could further enhance the user experience and the results.  

The concept of visualization could also be added because, currently, we need to use a separate tool. We use Business Objects for data visualizations.  

Competing products — like in case of Alteryx that is just in trial versions for now — has both machine learning concepts as well as some of the visualization capabilities within the data profiling features. SAS Guide will have to have these features to keep up with competing products and their capabilities.  

If these elements do pan out properly in Alteryx, I may like it better than SAS Enterprise Guide overall. I have a data science background but the more advanced data science features are new in these products and they are very useful in analytics.  

So SAS should work on ML (Machine Learning) features, visualizations, and even more on ease-of-use. It is good but everything can get better.  

I have been working with SAS Enterprise Guide for six years.  

We have not yet had any stability issues with the product. First of all, it is working well. Secondly, on the hardware side, we thought that we need to have more resources, so we did a capacity planning exercise involving SAS. They gave us some recommendations to improve our environment moving forward. We are installing a new setup on new hardware based on those recommendations. This should maintain our current level of stability and maybe enhance performance.

I feel that SAS has a good foundation. It has not given us a lot of problems when it comes to performance. We were being proactive. It works really well in combination with Teradata, but there is a lot of room for performance optimization with both tools.  

Using both platforms — and by that I mean featuring both SAS and Teradata — we do get our data out. In telco, the data volumes are huge, but even though that is the case we do not usually get stuck or experience stability issues.  

There are a lot of ways to get the performance optimization you need from products, get the work done, and delegate your time. It takes evaluation and revisions to accomplish those goals.  

There are many more business users in our organization, but there are only about seven people who really use Enterprise Guide as technical people. We do not see much of a problem with scalability for either group of users.  

Technical support is good. Whenever we have a problem, we have technical support sessions. It can be improved, I believe, but I would say that it is not bad either. It works for us.  

We do use other tools on occasion although we are presently focused on Enterprise Guide as our core in analytics. Ultimately the goal is to get the best results in the easiest way. If that requires changing tools, that may be necessary. Right now we are using SAS along with Teradata.  

The initial setup is a bit complex. For the guest access setup, that is something that we do ourselves. But when it comes to the server installation, that is where we require support from SAS. When it comes to the server installation, the configuration is usually done by SAS themselves. The setup is something that is certainly too complex for business users or people who do not have a technical background. It is usually good to involve SAS even if you are going to an upgrade. It can just make things go more smoothly and keep from wasting time with unnecessary issues.  

We just have one guy for the maintenance of the system. If there is a problem, then he can rely on support. There is only one guy because the product is pretty stable.  

When looking at different tools on the market for data analysis what you need depends on what you want to do. If you have a portfolio within your organization, you may feel that you will need a lot of other tools in addition to make a proper analysis. SAS is a broader solution. It is not just the Enterprise Guide. It has marketing automation and there is even stream processing. Then there is Enterprise Miner. Enterprise Guide is a suite and not just a one-dimensional tool.  

If you feel that you are ready to make an investment and you need the capability of advanced analytics within your organization, you will be able to utilize the whole tech stack with Enterprise Guide. If that is the case, then obviously I think you should go for SAS because it is a more mature and evolved product than most other products. In addition, the opportunities for integration with the SAS platform is really good.  

If you need a specialty architecture, you have Alteryx on one side and you have SAS on the other side. If you do go with Alteryx, then you have a separate campaign management tool and you may not be able to get the full benefit of an integrated solution.  

People considering SAS Enterprise Guide should also look at Alteryx. It is pretty simple. Some people on my team feel that Alteryx is actually much easier to use for what they do and that its interface is much better even if it is similar to Enterprise Guide. The overall availability of different kinds of features is much better in Alteryx.  

I think the first step of evaluating potential solutions should be to look at your roadmap. If you want to go start simple, then obviously you can start with any tool. But if you have a roadmap in place or if you are not a beginner, then I would suggest going for one integrated platform as a suite rather than multiple tools. Having an integrated solution is probably a better overall.  

On a scale from one to ten where one is the worst and ten is the best, I would rate Enterprise Guide as a seven overall. It is a good product but it lacks some important features other products are coming out with.  

The solution is mainly for bringing in a large amount of data. For example, let's say you have a retailer and they have various types of sales. They have stores both online and brick and mortar and they have sales happening in both places. What you're trying to do is decide all kinds of information based on the store versus online. Stores have different numbers of square feet and carry different types of merchandise depending on how they rank the store in different cities. If, for example, in Columbus, Ohio, if there are three stores, exactly the same store, they may be ranked differently based on the monetary intake that they have. Then there's the online information that they're pulling in, and data is being collected around who's ordering online and if they ordering versus going into the store, etc. All that data is pooled from the credit card information and it's cataloged. Trifacta allows you to write a code to bring that information together so that you can manipulate it at the end.

Once the information is collected, a data scientist can actually begin giving VPs in their departments the information that they need on the spot to make decisions about products. They can assess the information that the AI and machine learning is putting out and they can look at it and go, "Okay, you don't even need store C, so make store B larger, combine those two stores, give them different clothing in store B and they'll start to compete better in a market". It's amazing how much detail they can get in order to help make sales more efficient. 

The solution is extremely robust.

It works well for data scientists and can really help them drill down on the information in order to give management tangible data points for larger business decisions.

The solution can execute and connect better to platforms that are actually non-platforms like Pega.

This product really needs to improve its ease of use. 

The solution needs to do a better job of promoting and marketing itself. Alteryx is much better at this and as a consequence is more recognizable. Trifacta needs to showcase its technology better.

It truly should encompass the A to Z execution of how you execute business process modeling in an intelligent automation form.

A future release needs to be something that's easier to handle. I wouldn't say a layman should be able to use the solution as data wrangling is something that somebody has got to really be certified in. I have a friend who's worked at IBM for 30 years, and now he's just getting certified as a data scientist. However, if the solution was designed as something that really could assist a new data scientist or kids coming out of college, something that's easier for them to wrap their heads around, that would be helpful.

The interface on Trifacta is way too busy. I used to do UX and it's just awful. It looks a little bit like Excel. I can't stand it. The UI of Alteryx is much, much better.

I've worked with the product in the last 12 months or so.

The solution is a very stable piece of software. It doesn't really have bugs or glitches. It doesn't crash or freeze. It's reliable.

The solution is very easy to scale. If a company needs to expand outwards they can do so very easily.

I bever reached out to a help desk, per se, however, any time I ever needed someone that I could engage with at a much higher level, they were there immediately. That was nice of them, considering we never implemented an actual solution.

Alteryx is simpler because it has more of a drop-and-drag and it's more business process-oriented. So, if you have a business process that's already to execute Alteryx is easier. Trifacta you really have to have a data scientist to be able to look at the data, but Alteryx is more citizen developed and ready to go as long as they can learn the product versus learning the science.

Basically, Alteryx does that faster. Alteryx truly does that faster in a simpler format. Trifacta is a much more robust tool that executes more information, however, it is way harder to learn.

We never actually ended up implementing the solution for the client.

Finance is the biggest ROI advantage a client will get. It will help them make smarter financial decisions.

I was never part of the decision-making process when it came to pricing. I'm not sure of what the exact costs were.

As a partner, as a reseller, I don't know much about the Trifacta side as they were partners with them before I came on. Alteryx, for example, seems expensive to me, however, I came from RPA, which was not that expensive to implement. Once I got used to something like data wrangling it didn't seem too much for us. 

The number one concern for us was to become a partner. The resell seemed like the ROI was going to benefit the customer. I felt, just like with RPA, once you can show the value, (and you can show the value almost immediately because this stuff can be run overnight), the cost isn't too big. 

We are resellers. We work with clients to implement solutions.

I personally never executed the Trifacta solution. We did propose one, however, the client did not accept the solution.

Overall, I would rate the solution at a nine out of ten. I've been pretty happy with it overall.

This solution is primarily used for various data analytics in an enterprise environment. 

The reality of any data analytics project including Data Science is that 90% of the effort goes into data sourcing and preparation. Data usually comes from multiple sources including data warehouses, web scraping, Excel input, free text, etc. KNIME allows you to do the 90% plus other predictive functionality.

It is a free open-source tool that performs very similarly to other expensive tools. KNIME has been great for me over the years. It allows me to connect to various sources including data warehouses, then put the processing logic together (ETL-like), which can be quite complex and produce the required output. Ultimately, it would go into Excel or Tableau for presentation.

The features that I find most valuable are:

1. The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes.
2. Unlimited volume of data; you are only limited by the machine you run on.
3. Python and R integration.
4. Predictive functionality and text analytics. If it is not enough then you can use custom Python and R scripts.
5. Looping functionality.
6. Variables allow you to parameterize your flows.
7. Run one node at a time, which is something that Alteryx users dream of doing.
8. Managing (collapsing) sub-flows, which is another thing that Alteryx container users also dream of.

The areas that I feel need improvement are:

• It needs support for a joiner node to have three outputs (left unmatched, matched, right unmatched), as competitors do (have not checked 2019/20 releases).
• I need the ability to add additional comparison conditions to a join. For example, in SQL you can specify only rows with a date fitting within a date range from the joined file. At the moment in KNIME, you should allow a join explosion to take place and filter what you need later, but sometimes the output becomes too big.
• It would be helpful to have more examples of Java code for nodes, like Java Snippet.
• I would like to have this solution show row counts on canvas, as it would improve the control and speed to build the workflow.
• The pseudo-code types could be rationalised into one (e.g. only Java).
• I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script.

I have been using KNIME for between four and five years.

My system occasionally may crash like other similar tools, although autosave is available.

Scalability is limited to a desktop application.

Obviously, as an open-source application, your options are limited but I have found answers on forums when I needed help.

Recently I have been using Alteryx so I have collected a few points on differences in both tools. Both are good, I can conclusively say I could go back to KNIME and be as effective data professional as I am with Alteryx.

I have to use Alteryx due to my client's tool choice, but I know that what I am doing with Alteryx right now could be done better in KNIME. Of course, Alteryx has its own advantages for certain areas.

It is a relatively simple install. You can even avoid installing it and run from a directory.

KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website. The fact that KNIME is open source may create challenges from an IT security view in an enterprise environment.

For this review, I would include Alteryx and Lavastorm (the latter is no longer available).

If you need a good Visualisation functionality, you should use Tableau or something of that caliber. However, the data prep can be done KNIME, which would give you extra confidence that what goes into your Visualisation layer is correct.

Overall, KNIME is definitely worth considering.

Our primary use for this particular product is to provide it for a large medical research company. It is a research company that helps pharmaceutical manufacturers, distributors, and clinics with analysis. Our client collects data that they crunch to provide these types of companies information about trends in distribution and production within the pharmaceutical industry. They use RapidMiner to crunch that data.  

The results of the analysis of the data helps with pricing, it helps with determining volumes, it helps with projections, et cetera. The client has a global license for RapidMiner, so here in the Middle Eastern branch, they use the same product as they do globally.  

I think it is a fairly straightforward interface generally. It is an easy-to-use solution compared to SAS Enterprise Miner, for example.  

On the other hand, compared to some other products, maybe the UI could be enhanced. The visual interface could have something like the-drag-and-drop features which Alteryx already supports. Some of those additional features can make RapidMiner a better tool and maybe more competitive or advanced.  

We have been working with the product for maybe two years now.  

Stability is good with this solution.  

Scalability is not really a concern with RapidMiner. The implementation that I have seen of this product for our client now is very sizeable. It scales very well.  

I had previously used other solutions but not as a part of this company. It was a matter of who I was working for.  

The initial setup is something that is supposed to be straightforward according to the people who implemented the solution. I was not part of the setup directly, so I don't know how complicated that is hands-on and so I'm not sure exactly what is involved or the exact amount of time it might take. The initial data mounting might be a big task when it is done globally as it is for this client.  

The current licensing cost is advertised on the website. The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license.  

I used to work with a company where we sold a variety of products as system integrators. So I compared some product because we had to evaluate them to know the advantages and differences. At that time, we got to the stage where we were signing up with Alteryx as a partner.  

So I was in touch with those Alteryx guys and wanted to also compare their product with SAS Enterprise Miner, who we already partnered with, and with RapidMiner as well.  

A couple of customers were already using RapidMiner and we were in the process of partnering with Alteryx. We have to know the other products even if it is through secondary research in addition to what the vendors are presenting.  

On a scale from one to ten where one is the worst and ten is the best, I would rate RapidMiner as around a seven. I choose seven because of the UI things and other parts of the product that might be improved. RapidMiner is more of an enterprise product. Here, in this region, most people like a packaged solution like Alteryx which covers more. Alteryx is also more attractive to many users because it is cheaper and easier to use from the perspective of the user interface.  

With Alteryx or Tableau, for example, you can just pick up data sources and then start EDL (enterprise data lake). It takes more effort to bring the data on to the data mart for RapidMiner and other enterprise products in the traffic mining category. These enterprise solutions have an additional level of complexity and flexibility but not everyone even needs it.  

I believe we are using the new version.

Our company makes comprehensive use of the solution to consolidate data and do a certain amount of reporting and analytics. All the data consumers use Databricks to develop the information.

Data governance should be addressed. We have some trouble connecting all the governance solutions with Databricks. This means the integrative capabilities are problematic. 

The initial setup is difficult. 

We have been using Databricks for a year-and-a-half.

The solution is stable. 

The solution is scalable. 

The technical support is good. 

As we are talking about a corporate solution, the deployment of Databricks lasted longer than the one day it took for Alteryx. 

We used Alteryx prior to Databricks and continue to do so, it being the only other solution we have employed. We use the two with different software. 

The initial setup is difficult. 

While I don't know exactly how long the deployment took, I do know that it lasted longer than the one day needed for Alteryx. 

I believe we used a partner for the deployment, although I cannot say for certain, as this is not within my purview. 

I don't know how many people are needed for maintenance and deployment. 

As the licensing is not within my purview, I am not in a position to comment on this. 

My company makes use of the solution. It is employed by my data team and the technology one. I do not have personal experience using the solution. 

The solution is deployed on base, on data. 

I am not aware of how many people make use of it. 

I rate Databricks as a seven out of ten.