KNIME Business Hub Reviews

We are using KNIME for basic analytics to reduce the amount of processing time. We found that it takes a lot of time for scripting on the cloud, so we have been using it locally on our PCs.  

While the product has not yet improved our organization, we expect to use it in full deployments with our clients to greatly reduce their costs and make our services more attractive.  

The most valuable part of the solution is the machine learning part. The second feature that we use most is big data connectivity. When we deployed the architecture, we directed our IDS (Intrusion Detection System) server to where the big data will be on our servers. Then we needed some kind of basic machine learning and obviously. After that, we connected it with Tableau visualization. Now we are writing the big data part of our solution along with the overall machine learning. These two parts will be the most important for our business going forward.  

I think also connectivity with hybrid databases and also integration with languages like Python are great advantages to what we are seeking to do in our environment. We have been using these features extensively and we find them to be very valuable in achieving what we hoped to achieve with the tool.  

One thing that I found was that in the open-source version of the KNIME analytics platform, we see difficulties in scheduling jobs. If the scheduler could be updated in the open-source version, the software will be easier to schedule properly and to use efficiently.  

The second time that I faced difficulty using KNIME was with data processing time. When we use large chunks of data for local processing, the processing is very slow. We do not want to move these big data often. For me, it seemed that moving one gigabyte of data went very slowly. So, the second thing that I would really like to see is a better ability to handle large amounts of data locally with KNIME in an efficient manner.  

The third area that might be improved is that when we have a large amount of data — let's say like five gigabytes — then there is one panel completely ignored. The impact of that on the results of our data processing is not good. So I would really like to see the load balancing and the overall processing time substantially reduced.  

So the things I would most like to see are the ability to handle large amounts of data and improved performance in processing.  

We have been working with KNIME for about six months.  

We do not have many people using the solution in our company at this point because the tool is comparatively new to us. There are around three or four users right now. We do have plans to increase the usage and the number of users. We have been planning it because we have growth opportunities with some clients. The only potential problem is that right now, we are under-confident, in our capability to implement pure KNIME solutions without more discovery and testing. So, we are planning it to replace Alteryx eventually with KNIME. But as of now, we are just planning. We do plan to increase the usage in the future but we have not done anything yet regarding that.  

The initial setup was very straightforward. It was not complex at all.  

We deployed it, we installed it ourselves on our local system server.  

We have done a few projects with some of our clients in KNIME. In these cases, we mainly used KNIME because of its ability to work in a data center environment in an enterprise system. This was one of the most important things that we were looking for. The second point was that KNIME is an open-source analytics platform. The point is that if some client has less data or a relatively small database, then we can use the open-source platform instead of using Alteryx, which is fairly expensive. These are the options we advise our clients about.  

On a scale from one to ten where one is the worst and ten is the best, I would rate this product as an eight out of ten. I honestly do not feel familiar enough with this product that my rating is accurate as I need to be more familiar with it over time. On the other hand, I have used KNIME and other tools in a similar category — like Informatica and Alteryx. Informatica is purely a data warehouse software. Alteryx is something we use frequently. So I have used three ETL tools. If I compared KNIME with Alteryx which are the most similar of the three, then I think KNIME is much better for our purposes. Strictly as a comparison with Alteryx, I would rate KNIME as an eight.  

I am just considering whether to use it or not. I am trying it to determine whether it is helpful or not. So far, it can solve my data analysis problems and I think it's a powerful data analysis tool.

It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop.

They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning.

The stability is great.

Most of the time it can solve the problems.

I have not used KNIME for a very long time so I have not used technical support so far.

Previously I used some programming tools, but I needed to do a lot of coding. KNIME is simpler to use.

The most important factor when I'm looking at which vendor or product to go with is the program's features.

I think the setup is straightforward.

I would rate it at nine out of 10. It's good, it makes thing easier.

I mainly used it to perform predictive modeling projects, such as customer-churn predictions and HR attrition predictions. The environments are mainly SQL-databases or CSV files.

The installation I worked with to perform the analyses was a regular laptop with no computational server behind it, which may have an impact on the capacity of the program handling very large databases or files.

The clients I performed the analyses for were all very pleased with the results. For churn prediction, one of the companies proactively started contacting clients with high risk to churn, resulting in drastically decreasing churn rates.

For organizations with a small team of data analysts or data scientists, it is a very easy tool to become familiar with predictive modeling, and makes it possible to hand over projects to colleagues without the need to extensively document them.

• The very easy-to-use visual interface
• Help functions and clear explanations of the functionalities and the used algorithms
• Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis

For inexperienced analysts or data scientists, it is a very easy tool to take your first steps in modeling and analytics.

The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R).

The program is not fit for handling very large files or databases (greater than  1GB); it gets too slow and has a tendency to crash easily.

I used it quite intensively for 10 months, long enough get familiar with it, to follow training, to use it in in several projects, to ask questions on the user forum.

We use KNIME for two main reasons:

Automation: The main purpose of our utilization it to run scheduled workflows (such as CRM campaigns, business reports, etc.) on a recurrent basis. We have created ETLs to automate most of the recurrent tasks and we let it run via batch files.

Ad-hoc business cases: We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders.

In addition to leveraging KNIME flexibility to query data from our database for ad-hoc business cases, the product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine. In some particular cases, my team builds the workflow and then we hand it over to the stakeholder, who can run it on his own by inserting a variable or changing a few settings in the workflows.

• Easy to connect with every database: We use queries from SQL, Redshift, Oracle.
• Easy to have a clear view of the data at every single step of the ETL process, with the consequent possibility of changing the flow according to your needs.

I think the data visualization part is the area most in need of improvement.

I’ve been using KNIME for more than four years now. I started using it in the company I was working for in Rome (Paddy Power Italy), then I moved to headquarters in Dublin (Paddy Power Ireland/UK) and started using it for their business. Eventually, I moved to the United States and started using it for my current company (TVG-Betfair) and it is currently the main analytics tool in both our offices (New Jersey and Los Angeles).

I would definitely rate it a nine out of 10. I am very happy with the product and it would be hard to find something better in the market.

All analytics individuals use KNIME to integrate multiple sources of data (SQL, excel, etc.) and prep the data for static reporting.

We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics.

• Visual workflow creation
• Workflow variables (parameterisation)
• Automatic caching of all intermediate data sets in the workflow
• Scheduling with the server

The overall user experience feels unpolished. 

• Data field type conversion is a real hassle, and date fields are a hassle.
• Documentation is pretty poor.
• User community is average at best.

It is pretty stable.

Partially, only with very large datasets (10M+ records or so); its reliance on RAM is a bit high for normal PCs. Servers should be fine.

Not applicable (not local in South Africa).

Alteryx. KNIME is much cheaper. The KNIME desktop client is free. KNIME handles 95% of our requirements.

Straightforward.

KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required.

Alteryx.

I rate it a seven out of 10. It's very useful but needs polish and improved UX and UI in several areas.

For quick adoption, either get KNIME to provide training, or have a local knowledge expert on hand who is well versed with data workflow tools, and databases if necessary.

Primarily used for advanced analytics, include designing and running predictive models, and conducting segmentation analysis. With KNIME, I connect to different data sources but usually need to conduct some data transformations before the main task is carried out. My results are usually written to a database, then I use a different tool for data visualization

It has allowed us to easily implement advanced analytics into various processes.

Easy to use nodes for ETL processes. This is because, in many cases, I usually transform the data before the main task even when the data is from a structured database.

Data visualization.

I write weekly articles breaking down the previous week's Green Bay Packers' game. My main use for KNIME at this time is a workflow that takes play-by-play data from a CSV and puts it into a multi-tabbed Excel document, with all the stats I need for the week.

I had been doing this via a mix of Excel macros and some things by hand. Even with the macros, it would take me 30-plus minutes every week, and even that was just for the raw data to get to pivot tables. If I wanted additional calculations based on pivot table data, that would take even more time. With KNIME, I am able to get that process down to under one minute, with data broken down into individual tabs. It has changed my week.

I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data.

I'd like something that would make it easier to connect/parse websites, although I will fully admit that I'm not as proficient in KNIME as I would like to be, so it could be I'm just missing something.

No issues with stability.

No issues with scalability.

I haven't had to use technical support, but I have been able to find answers in the forum to any questions I have had.

I had been using a combination of Excel macros and manual entry. I switched because I was looking for something a bit quicker and automated, something to remove as much human error as possible.

I started simple, as I was learning the software as I went. It ended up being fairly complex. I still had some manual entry, but as I learned what KNIME was capable of, I kept building more and more to get everything as automated as possible.

I ran through a couple different options. None of them matched up to what KNIME could do.

Do training up front to make building workflows clean and easy from the start.

Providing the right solutions and consulting in revenue management requires rapid and comprehensive analysis in all areas. Such analysis makes it easier to look for patterns, where and when the cause of a problem is, especially when the solution has hundreds or more servers of different types and characteristics.

I use KNIME as a tool (ETL) in processing various logs and data (structured and unstructured format) then analyze and store the information in a database. This makes it easier to do the analysis and saves me time.

It very much helps me to do my job while supporting my organization's delivery of service to our client.

Most important, it is open-source. Next is the ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database.

I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports.

In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations.

I feel the query performance is slower than my old code. In my configurations, I set concurrence for a heavy query database, from multiple database sources, then transformed it before loading it into a destination database. It cannot do concurrent writing into databases if I use one database connection (user).

I’m not sure it is a lack in KNIME or in the database driver itself. To prevent the degredation of performance and system stability, I need to change the configuration of databases readers for output, write parameter onto the disk, not into memory.

I have never used tech support, but the community forums are quite good. Hopefully, there will be a knowledgebase, like VMware did.

I created my own script. I switched to KNIME because it simplifies the flow of my script into one workspace, and doesn't necessitate a lot of jobs in my system.

No, KNIME is my first choice because it's open-source and has features to combine with other scripts.

If you like data analysis, KNIME is the best option. It's free and easy to set up.