KNIME Business Hub Reviews

We used the solution for data analysis. With the help of its graphical workflow interface, we were able to identify the exact logic behind the source code.

The solution is the best data science tool if you gain in-depth knowledge about its functions.

They should improve the solution's integration with other platforms. Also, they should add more functions to its server.

We have used the solution for less than a year.

I rate the solution's stability as a seven as we face memory leakage issues.

We have nine users of the solution in our organization. I rate its scalability as a nine, as we can deploy it in a cluster.

We used Rapidminer earlier. We switched to KNIME as it is a better tool for data mining.

The solution's initial setup is easy. The process takes several minutes to complete with a good network connection.

We have a technology tool to help us install the solution.

You must have the essential knowledge to solve the solution's compliance issues. It could be easier to use and integrate with other products.

I recommend the solution to others and rate it as a seven.

KNIME is mainly used for developing workflows for machine learning and VA. We are using integrated Python scripts.

The solution has improved our company because it is economical as it is an open-source platform. There are not a lot of costs involved.

The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way.

I want to access more of the popular deep learning libraries or frameworks as they have in other solutions, such as TensorFlow and PyTorch. There should be better integration, and a better way to use those libraries because they are very popular. Our company uses PyTorch and TensorFlow regularly to solve many computer vision problems. We expect better integration of those two tools.

KNIME could improve when it comes to large data markets.

I have been using KNIME for approximately one and a half years.

The solution is stable but it could improve.

I rate the stability of KNIME a six out of ten.

We have approximately 25 people using this solution in my company. The employees typically do business analytics with MLS and DLLs in their workflow.

We are not using the solution at its full capacity.

I rate the scalability of KNIME a six out of ten. 

I have not contacted the support from KNIME directly. We use online forums and blogs.

We used Alteryx prior to KNIME.

We do the deployment of the solution in-house. We have a team of approximately five to seven people who not only take care of these pipelines, and workflows in this solution, but also do a lot of AI and ML module developments.

This is a free open-source solution.

We selected KNIME rather than Alteryx because of the price comparison.

We usually only use the coding methods using Python. When people don't have much experience with coding, then we always recommend they choose the concepts of analytics and machine learning, mainly a no-code tool, such as KNIME or Alteryx. They become close to citizen developers. We will teach them coding aspects later. They always start with a no-code or a low-code tool, such as KNIME and Alteryx. Once they learn slowly, we switch them over to coding access.

I would recommend this solution for use cases that has a small amount of data and not a lot of code needed.

I rate KNIME a six out of ten.

We use KNIME for data and manipulation.

We have found KNIME valuable when it comes to its visualization.

We have been using KNIME for the past five years.

KNIME is stable for our needs after using it for five years.

KNIME is not scalable.

I have had no need to contact technical support.

I have used Python. The logic is the same, but the interface is totally different.

The setup for KNIME is simple. I would rate the setup a five on a scale of one to five.

I would rate KNIME a seven on a scale of one to ten.

KNIME is used for collecting data for data science.

KNIME has improved our organization because we are able to collect data in a way that we can interpret and it provides visuals.

Overall KNIME serves its purpose and does a good job.

KNIME can improve by adding more automation tools in the query, similar to UiPath or Blue Prism. It would make the data collection and cleanup duties more versatile.

I have been using KNIME for approximately four years.

KNIME is stable.

KNIME is always highly scalable, as new features come out and things that we learn from the new features can help us interpret data better.

We have approximately 12 people using the solution. There are three groups of users, the data entry team, data collection experts, and data interpreters. They use the solution for two months for each project we work on. We are planning on hiring more data scientists.

I rate the support from KNIME a three out of five.

I have not used other solutions other than KNIME.

The initial setup of KNIME was not challenging, but it took some practice.

The full deployment took us approximately one year. It took us a little while to adjust to the solution because many of the users did not have an analytics background, we had to teach them.

I rate the ease of setup for KNIME a four out of five.

We did evaluate other solutions. We decided to use KNIME because it is open source and was easier to start with.

My advice to others is to start out slow and gradually increase operations. 

I rate KNIME an eight out of ten.

There are other applications that I've used that make collecting the data and interpreting it a lot easier. If they could improve this my rating would increase.

We are users of KNIME and we also resell this product.

The primary use case is for data analysis.

KNIME has a good set of features for data analytics.

The sampling features are some of the most important ones for us.

Compared to the other data tools on the market, the user interface can be improved.

There are quite a few features that are not available in the Community Edition. Having more of these features brought into the platform would be helpful.

Support from KNIME could be enhanced, although the community support is great.

I have been working with KNIME for between three and four years.

It has been stable for a while, since the release of version 4. However, between version 2 and version 3, it was quite unstable. The stability has improved.

KNIME is quite scalable, which is one of the most important features that we found.

The support for KNIME is good, although it comes from the community rather than from KNIME itself.

The initial setup is quite simple, especially if you have already worked on other tools.

The deployment can be completed within a couple of hours, including the setup of permissions and other configurations.

There is a Community Edition and paid versions available.

For anybody who is looking for a new data science platform, KNIME is a product that I can recommend.

I would rate this solution an eight out of ten.

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.

We used the product to prepare data for our team. I would prepare SQLs and check them in Oracle Developer, then create workflows in KNIME to manage and process the data, creating specific tables for modeling.

The product is a great alternative because it is not an open-source tool and offers simplicity, making it easier for our large team to use.

Sometimes, we needed more space to handle larger operations, especially since our machines had limited space and memory due to Kubernetes clusters. Breaking up SQLs was necessary to handle the data flow better.

I extensively used KNIME for about one year and at least two months.

The product is quite stable.

The platform is scalable. It is possible to configure the system to effectively manage memory and space requirements. 

I rate the scalability a seven out of ten. 

The community support is good, and plenty of shared knowledge is available.

We had licensing issues with other tools, but KNIME worked well as an alternative.

We integrated KNIME with Oracle, Apache, and other tools. It allowed us to pull data from various sources, such as Oracle, CSV, and Excel, into one consolidated table, which was very efficient.

Overall, I rate it an eight. It is a good tool, especially for our current requirements. However, there were limitations, such as space issues and occasional process slowdowns due to memory constraints. Despite these challenges, it is a solid product.

I recommend it to other professionals, particularly those who work with diverse datasets and require a flexible tool to manage data flows. It is user-friendly, especially for individuals with a background in Java or Python, as it allows for custom operations and automation, which I found very helpful in my experience.

At our company, we provide consultation services to federal institutions, and in some cases, we recommend KNIME to clients and have also implemented the solution for a few projects. 

The graphic features of KNIME need improvement, especially when working on dashboards. 

I have been using KNIME for three years. 

The stability of KNIME depends on the resources on the computer in which it's installed. KNIME requires a high capacity of RAM and memory; if the hardware needs are fulfilled, then the product shows satisfying stability. 

When I used to work on KNIME every day, I faced some issues, but they were quickly resolved with the help I received from the active KNIME community. 

Our organization has about ten KNIME users, and the solution is used around once a month. 

I would rate tech support a nine out of ten. 

Positive

I have experience in Qlik Sense, Tableau, and native Python programming. I personally switched to KNIME because my present employer had KNIME already implemented in the company. 

The initial setup of the solution is easy. Installing the KNIME Analytics free version on a desktop takes only a few minutes. The setup and installation process is quite easy to begin. 

The implementation was carried out in-house. The solution is free to use; therefore, a partner or reseller is not needed. For the KNIME Business Hub, our organization is directly communicating with KNIME, and a reseller isn't required. 

KNIME offers a free version to use. KNIME also has a server version with almost the same price as competitors. 

In data analytics workflows at our company, we use KNIME for the preprocessing of data, and for a few projects, an ETL track was built using KNIME. We are planning to use KNIME Business Hub to create data apps where a dashboard of analysis can be prepared and presented to others. 

Our company mostly uses KNIME for small datasets, projects, and customers who don't have much expertise in data analysis. We introduce KNIME to our organization's customers to get started with data analysis. The solution is very easy to use. For advanced data analytics, I use a tool different from KNIME. 

The product has impressive data-blending capabilities. The solution is user-friendly and helps our company easily onboard new users. I would definitely recommend the product to others, majorly to individuals with beginner-level programming and data analytics skills. I would rate KNIME an eight out of ten.