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KNIME Business Hub - Basic Edition
KNIME Business Hub on AWS provides a single environment for all data workers to collaborate on and deploy data science solutions across the organization.
Ratings and reviews
4.1
27 ratings
3 AWS reviews
|
24 external reviews
External reviews are from PeerSpot .
Reviews (27)
Marta
Automated document workflows have transformed manual paper records into minutes-ready analytics
Reviewed on Aug 18, 2026
Review provided by PeerSpot
What is our primary use case?
I automated the processing of paper-based data by digitalizing scanned documents and creating a KNIME Business Hub workflow that transforms the data into an analysis-ready format, reducing a manual task that would have taken hours or days to just a few minutes. This designed and implemented workflow supports fast, accurate response to prosecution.
This year, with better knowledge of KNIME Business Hub and improved skills in using it, I automatically scanned and completed the process automatically, which took a few minutes instead of a day or months. This represents a significant improvement in the time I need to analyze data.
What is most valuable?
One of the best features of KNIME Business Hub is the ability to share, reuse, and collaborate on workflows. I can share my workflow with my team, and they can improve it. It provides access to a large library of pre-built workflows and components which promotes development and best practices across the team. I also appreciate how it supports versioning and centralized management, making it easier to maintain analytic solutions.
Regarding how the collaboration feature helps my team in our daily work, we often use our teammates' workflows because when someone has already prepared something, there is no need for other people to do it again. We share and improve the workflows together. This is a best practice that has also made our job faster.
KNIME Business Hub has had an impact on the organization by improving collaboration, standardizing the data process, and reducing manual work. It also provided a centralized environment where workflows could be shared. In my experience, automating the processing of scanned paper records with KNIME Business Hub reduced tasks that would have taken hours or days to a few minutes. The Hub also improved governance and transparency by making workflows easier to monitor and document.
What needs improvement?
KNIME Business Hub could be improved by making the user experience more intuitive, especially for users who are new to data analytics. More guided features and documentation would be beneficial.
For how long have I used the solution?
I have been working in my current field for about one year.
What do I think about the stability of the solution?
I did not have any issues with KNIME Business Hub; it is stable.
What do I think about the scalability of the solution?
KNIME Business Hub works just as well for processing much larger amounts of data or having more people using it at the same time. The biggest advantage of KNIME Business Hub for me is that I can start with a simple workflow and gradually scale it into an organization-wide solution, and it works. There is no problem with that, whether it is a simple workflow or it is gradually scaled.
How are customer service and support?
I have never needed help with KNIME Business Hub. I always find a solution in the FAQ or my teammates help me.
Which solution did I use previously and why did I switch?
I was working with what I would call a Stone Age solution: scanning documents and manually processing the data. I decided to switch to KNIME Business Hub to automate the process and save a significant amount of time.
I did not evaluate other options before choosing KNIME Business Hub. I saw KNIME Business Hub at one of the conferences I attended and chose it.
What other advice do I have?
My advice to others looking into using KNIME Business Hub would be to start experimenting. Do not be afraid of KNIME Business Hub, even if you do not have a strong programming background. Start with a simple, real-life problem and build the workflow step by step. KNIME Business Hub makes it very easy to see what is happening with your data at each stage. Once you see how much time you can save by automating tasks, you will naturally want to explore more. For me, KNIME Business Hub changed the way I work with data. Instead of doing repetitive manual tasks, I can focus on the analysis and the results. I would rate this solution a ten out of ten.
Robert Krebs
Centralized data apps have enabled widespread self-service analytics and collaborative automation
Reviewed on Aug 16, 2026
Review from a verified AWS customer
What is our primary use case?
There are several main use cases for KNIME Business Hub. I am an administrator of KNIME Business Hub for various colleagues at Siemens Mobility, which operates in the transportation business. Most use cases revolve around finance and commercial topics, ranging from creating data apps as dashboards to data apps as self-service tools that allow colleagues to execute workflows and receive output. We also have use cases around data quality management, where inputs such as project risk registers are processed through workflows that use large language models to generate proposals on how to improve project-associated risks. This capability is widely used by commercial project managers within Mobility.
Other use cases include typical ETL processes, where data is extracted from various sources, transformed, and loaded into Snowflake, primarily relating to finance data. There are also use cases in engineering, where we make use of Python libraries within KNIME. This introduces some pro-code elements, where we extract data from PDFs and make it accessible in properly formatted databases in table formats. In finance, many people work with Excel files, and many use cases aim to eliminate VBA and automations within Excel by translating them into transformations in KNIME, because the visual approach makes them much easier to maintain and continue developing, especially for business users.
I am not developing use cases for myself or my team with KNIME Business Hub. Rather, I am enabling others to do so within Mobility, mainly colleagues in finance—especially controlling and commercial project managers—but also colleagues from the engineering workspace. We have had impactful use cases from colleagues who work with technical drawings that require optical character recognition, where data can still be read using useful Python libraries. This is especially valuable with the Data Apps approach, which I believe is the unique selling point, as it gives consumers of the data apps the option to input data and receive output. This is particularly useful since consumers are not charged by KNIME. Only developers require a developer licence, but an unlimited number of consumers can use it. This really stands out, as many people can take advantage of it—both for self-service, where input data is provided, and for receiving output via dashboards. The interactive approach with Data Apps is truly useful.
The main point I want to convey about how my colleagues are using KNIME Business Hub is the ability to expose workflows in different ways. One way is through data apps, but it is also very useful to expose them via API or to create custom servers. We have colleagues doing this who are able to integrate KNIME workflows with their existing external applications and make use of them via REST endpoints. We also have a few use cases where workflows can be triggered by events—for example, when a new Excel file is uploaded to a folder or mount point. However, most workflows are primarily scheduled via time triggers, which is probably the most common execution method.
What is most valuable?
Data apps are definitely valuable because of the consumer approach with KNIME Business Hub, which makes them cost-efficient and scalable. One or two developers can work with subject matter experts and the business to develop them collaboratively, and then make them available while managing who has access to the workflows and who can use secrets within them. This has the most impact because they can be scaled not just for one team or department, but across business units.
With the local KNIME Analytics Platform, everyone was only able to execute workflows on their own machine. We had workarounds where we used our own server and executed workflows on a time trigger. However, the capabilities around data apps and exposing them as REST endpoints have added tremendous value. Data apps are truly impressive, and people use them widely. We also use them for some use cases with LLM components in the background, and people really appreciate this. They can also be used in coordination with other tools. Since I come from a UiPath and RPA background, we use them in that combination, and most people are quite satisfied when everything works as intended.
What needs improvement?
How KNIME Business Hub is set up matters. In our case, we had one team assigned to Siemens Mobility, and within that team, we set up various private spaces. Unfortunately, it was not possible to assign roles and permissions to these private spaces. This was raised with KNIME, because if you have different teams and want them to manage these spaces individually, it would give them much more ability to operate independently, and we would not have had to rely on one central team. This should definitely be improved. For us, this meant we had to manage all secrets on their behalf, as there was no way to do this at a folder level, which in my view would correspond to a private space. This is something that would be available in UiPath. Ideally, teams would be able to manage folders on their own and have folder-level administrators, with different roles for users at the folder or team private space level so they could create and assign their own passwords or secrets. Currently, this can only be done at the personal level with personal secrets or at the Siemens Mobility level, which encompasses all folders.
There is another area for improvement, although I cannot comment on it in great detail. Our setup involved our IT team hosting KNIME on AWS / Kubernetes and handling all infrastructure configuration. We observed that resource allocation was sometimes not done properly, which resulted in frequent hiccups where resources were unavailable and workflows were not executed on time. This required involvement from our IT team, KNIME, and our own team to resolve. I cannot say whether it would have been better to use the SaaS solution, where KNIME handles infrastructure setup and maintenance, or whether something could have been done better by our IT team. In the end, it was suggested that we purchase dedicated vCores on our side, which would have driven up costs significantly and was not feasible for us. We went with shared resources. From my perspective as an end-user administrator, these issues occurred too often. I have no real insight into who is responsible or whether it is truly necessary to purchase additional vCores when resources are insufficient. Additionally, we had some problems with the scope of executions, though this did not affect many colleagues and was more common among those with more sophisticated setups.
The missing granularity regarding permissions and the issue of having sufficient resources available—sometimes causing KNIME Business Hub to be unavailable—are the two main problems we have encountered. I am uncertain whether these are KNIME issues or IT issues on our side.
KNIME Business Hub's AI capabilities are not on par with other tools. Although other tools may not be directly comparable, I am an expert in UiPath and we have strict requirements. We tried to convey our needs to KNIME, but KNIME is not at the point where we could easily integrate it into our environment. From a governance and security perspective, KNIME is not where it needs to be to become part of our AI factory setup.
Regarding KNIME Business Hub's AI capabilities, I cannot comment in depth. We have not truly used the AI features when it comes to reliably accurate outputs. We mainly use LLMs in the KNIME Business Hub for the KNIME AI Assistant. We have configured three large language models so that users can use the AI assistant from KNIME Analytics Platform to support their development work. Beyond this, we have not made significant use of KNIME Business Hub's AI capabilities. The only AI-related work we do is at the KNIME Analytics Platform level, where we use nodes for mainly local AI tasks such as LLM prompter nodes or LLM chat prompter / Agent Chat View nodes.
For how long have I used the solution?
I have been using KNIME Business Hub for about one and a half years.
What was our ROI?
I do not have the exact numbers available, but we collected information from the different teams and organizational units, and I know the savings were in the millions of dollars.
What other advice do I have?
I would strongly recommend reviewing the training materials available for KNIME Business Hub. It is important to involve your IT team as much as possible and to pay close attention to available resources and resource consumption. Staying in close contact with KNIME is also essential to ensure timely support, as otherwise too much time can pass before problems are resolved. I am still uncertain whether it is better to use the SaaS solution from KNIME or to host it internally—both options have their pros and cons. From my perspective, I would lean towards the SaaS solution, though it most likely depends on the capabilities of your IT team. Overall, I would rate this product an eight out of ten.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Pierpaolo Vergati
Data workflows have transformed decision making and provide scheduled insights for every team
Reviewed on Aug 16, 2026
Review provided by PeerSpot
What is our primary use case?
KNIME Business Hub's most valuable feature for me is the ability to schedule workflows periodically—by day or every X hours, for example every six hours—to maintain completely updated business intelligence tools for my use case. This scheduling capability is the most important feature that should be highlighted when discussing KNIME Business Hub.
What is most valuable?
KNIME Business Hub represents the logical next step after becoming a KNIME user because it offers numerous features that are not possible with the KNIME Analytic Platform license. With the free, open-source license, it is impossible to access features such as workflow scheduling, workflow sharing within a team, and building KNIME Apps to provide analytics to colleagues. These tools enable team members to deploy analytics and discover valuable business intelligence insights directly by uploading data tables or datasets from their workflows.
The ability to share workflows directly in a space and deploy that space to add other colleagues and teams so they can deploy their business insights using KNIME Apps is very useful. Sharing workflows and deploying spaces is completely valuable to me because not all my colleagues are comfortable using the KNIME user interface, even though it is very intuitive. Sometimes they lack familiarity with this type of tool. Therefore, having a specialized team deploy KNIME Apps to help other teams and colleagues obtain their business insights directly is very important. This approach serves as a first step to share knowledge about KNIME's usefulness and may encourage other colleagues to start using KNIME.
In my case, KNIME Business Hub has provided better insight into the problem of having data but not utilizing it. This is the most important lesson I have learned from my colleagues and myself. It is important to learn how to use data properly in order to gain knowledge and deploy information into business decision workflows. This would be impossible without being a skilled programmer, but KNIME's no-code or low-code approach helps all colleagues who are not programmers.
What needs improvement?
I believe it would be beneficial to offer a free bundle for users starting to use KNIME Business Hub for the first time. Having a bundle to help colleagues who are not familiar with KNIME would be valuable, as requiring these new colleagues to pay for KNIME Business Hub from the beginning may not be economically feasible.
For how long have I used the solution?
I have been using KNIME Business Hub for approximately one month each time I receive a free bundle from my account because I am a KNIME certified trainer. KNIME periodically invites their most important users to preview new features, and to compensate for the time involved in reviewing new KNIME Business Hub developments, I periodically receive a free bundle. These bundles typically last about thirty days. I started using KNIME Business Hub periodically approximately one year ago, when I received my certified trainer certificate from KNIME.
How are customer service and support?
I find it very interesting how KNIME works to make KNIME Business Hub more accessible through their YouTube channels. Additionally, other KNIME users create their own YouTube channels to share knowledge about KNIME. KNIME's community is so active that it is impossible not to receive help when posting a use case or problem that you cannot solve.
The technical knowledge and documentation are quite impressive through the YouTube channel that I regularly check for newly uploaded videos. From my perspective, this is completely sufficient.
What was our ROI?
We have achieved more data-driven decisions since using KNIME Business Hub. For example, we collected and created workflows to analyze the energy consumption of our headquarters building, which helped significantly improve our energy efficiency.
What other advice do I have?
I would advise others to start with a few simple workflows and then increase sophistication over time as you gain experience. I would rate my overall experience with KNIME Business Hub as a five out of five.
AltanAtabarut1
Centralized analytics has streamlined model deployment and accelerated return on investment
Reviewed on Apr 21, 2026
Review from a verified AWS customer
What is our primary use case?
I mainly use KNIME Business Hub currently for data ETLs and then it meets with predictive analytics. Sometimes I utilize it for forecasting, but mostly it's predictive analytics.
I have utilized both model building relevance nodes in KNIME Business Hub, and there is the capability of deploying the model on the virtual PC so that everybody can utilize the model and send a query. Essentially, without an expensive decision engine, you can just deploy your model and utilize it, whether it's in an app, in something else, or on a website, it doesn't matter. You can just ask a question and the predictive model runs and you can get the results.
What is most valuable?
Collection of company-wide information is one of the main benefits that KNIME Business Hub provides to the end users; all the intellectual property that has been developed in a central location is critical. When somebody leaves the company and another one comes in, you can have all the information on KNIME Business Hub about what's working so far, what's deployed, and other relevant details. That actually provides a lot of information, so instead of doing something on your own computer, it's all centralized. It actually increases the general maturity of the company, including not only the user but the general maturity of the company and the line of businesses. All of the workflows are stored; I know who utilizes it, who built it, when, and how frequently it's used. A lot of insights are important, but in general, I have a lot of intellectual property built-in so I can keep track of it. It's very similar to GitLab, but this is for analytics and for the company itself.
On the computer vision part within KNIME Business Hub, it's not that capable, unfortunately. I utilize some of the existing nodes, but other than that, if I am handling classic tabular data, it's fine. If I'm handling unstructured data sets, I can push it to large language models and get some results back, and that's also fine. However, when it comes to visual analysis or vocal analysis, that lags a bit.
Integration capabilities of KNIME are almost simple. I have integrated with enterprise resource planning systems, SAP, and all that. With SAP it is working well, but it could have been better.
What needs improvement?
I would describe KNIME Decision Hub as somewhat helpful in making data-driven decisions more efficient. It could have been a scalable decisioning as a service at the back end, but it's not working that way. I can deploy a model and get some results by sending some queries, but if I happen to utilize it for a more scaled-up business such as a bank or telco, that is a different situation. Then, I start requiring hundreds, thousands of dollars worth of licensed software, and I wish it would be capable of scaling up and down as needed.
Visual analytics is the main point for improvement for KNIME Business Hub. Computer vision is the most important because now there is a new age of large language models and visual language models. The visual language models can turn an image or a video into text, and you can utilize it as if it's a very capable computer vision model. It tracks all the segments and all the labels on the images or videos, which means that if I can interact with these through KNIME Business Hub, then I can build very sophisticated analytics end-to-end. Right now, that's not that much possible, but I wish it's going to be in the near future.
If we talk about functionality, I would like to see most of the classic independent large language models and visual language models integrated into the next version of KNIME Business Hub. There are new multimodal capabilities in it, and I have to go grab a model from the open-weights structure, implement it somewhere, and send some queries. I wish that most of that would have been built-in. Databricks, for example, built models embedded into their software structure so that I don't need to go to a third-party; I can run some models to enrich my data. This includes enriching images into tabular data sets or converting voice into tabular data sets. Many enterprises cannot just push the existing data outbound and get some results back since a lot of that consists of internal data sets.
For how long have I used the solution?
I have been working with KNIME Business Hub since 2021.
What other advice do I have?
Most of the cases that I utilize from KNIME Business Hub require data from databases with semi-structured, almost clean data sets. However, more and more required data starts coming from Internet of Things devices, and these are streaming data sets, not static data sets living in databases. I need event listeners that listen to something, on-the-fly score it, put it into context, and send it to a large language model. Complex event processing is becoming much more of a requirement, and in that case, I need milliseconds of performance. KNIME Business Hub lags there a little bit, but it's improving.
First, I deployed KNIME Business Hub on-premises, such as on my machines, because it is low-weight. I can immediately install it onto my personal computer or even my tablet, run some data on it, and get some insights immediately. I don't need a huge footprint or an expensive hardware-plus-software combination. That's a good aspect. But at some point, if I want to excel in a company with one hundred users or fifty users, it has to be on either my own servers or a private cloud.
KNIME Business Hub can also be deployed on Amazon Web Services, Azure, or Google Cloud Platform based on the client's requirements. Amazon Web Services is the best option, but I haven't seen anything I can purchase from Amazon Web Services's marketplace. That would be a good benefit for any client to onboard immediately. Just click it down from the marketplace, and if I have corporate credits, I could utilize it there so that suddenly everybody at a bank, for example, owns a KNIME Business Hub license. That would be awesome.
Pricing for KNIME Business Hub is much cheaper than the competitors. It's a reasonable amount of money for the product.
I am already advising many companies to start machine learning and artificial intelligence integration with KNIME Business Hub because a lot of the large language models need to be integrated with classic basic machine learning to turn it into something called composable artificial intelligence. I generate a lot of data from text and video, push it into a machine learning model, push it to a forecast, get the results, and then export it with a large language model, talking as a normal person would. This is the composability part of it. If you want to do composable artificial intelligence and get return on investment fast, go with KNIME Business Hub. Don't go to software as a service, Databricks, or IBM Watson. These tools are both expensive and very capable but will take months to build something and get positive return on investment, leading to months in negative return on investment. With KNIME Business Hub, you can immediately grab some data and see positive return on investment. They have single-user free licenses, so I can start now on my Mac, throw some results, and do a proof of concept or proof of value for upper management in just a few weeks. Then I purchase KNIME Business Hub, and the company begins benefiting, leading to immediate positive return on investment. That is the difference that many don't understand. I would rate this product nine out of ten.
reviewer1515237
Workflow automation has saved time and simplifies database queries and Excel reporting
Reviewed on Apr 01, 2026
Review provided by PeerSpot
What is our primary use case?
My use case for KNIME Business Hub includes automation, querying from the database, and outputting to Excel and creating charts.
What is most valuable?
In my opinion, the most useful functions or features in KNIME Business Hub are its easy-to-use database query configuration, which requires no programming, although I use some Python as well. However, I sometimes prefer not to use Python because it is easy to use KNIME Business Hub.
I believe the main benefits I receive from KNIME Business Hub are automation. When I work through the workflow one time, I can reuse it later on, saving considerable time for many tasks. I only need to work through the workflow once and then I can reuse it quite easily by configuring some parameters in the node. The automation is quite helpful.
What needs improvement?
Regarding integration capabilities, I do not think it is that easy to integrate KNIME Business Hub with another product because the connector does not have many options. For example, if I want to connect to some OpenAI API, I still cannot find solutions for that.
I do not use the collaboration features within KNIME Business Hub, and I think the trend for data is more oriented toward generative AI. I believe KNIME Business Hub needs to catch up with this trend. If you want users to use generative AI, then you need to provide the features for generative AI.
In my opinion, KNIME Business Hub can make improvements by having more integration with Python or JavaScript, especially to work with generative AI better.
I would like to see additional functions in KNIME Business Hub that can connect to generative AI, allowing users to describe the workflow for easier workflow generation and creation. Normally, not all people know which nodes in KNIME Business Hub can be used, so if you describe the workflow, it would help to draft at least the nodes, making it more helpful.
For how long have I used the solution?
I have been working with KNIME Business Hub for five years.
What do I think about the stability of the solution?
From 1 to 10, I would rate the stability of KNIME Business Hub quite good, around an 8 or 9.
What do I think about the scalability of the solution?
In terms of scalability, I think KNIME Business Hub is around an 8.
How are customer service and support?
My mark for technical support for KNIME Business Hub is about a 7, as most of the support is in the community, and it is quite good because it is open source. Most people go through the community to get help, and it is quite easy to get examples as well.
How was the initial setup?
For the setup of KNIME Business Hub, I think it is quite easy because you do not need to have many things to set up. However, sometimes I find there is something wrong. For example, even though I save and then come back, it says something is wrong, and I have no idea why such things happen. Whenever I save something and then come back, it is just that some of the nodes have been deleted, which is very rare.
What other advice do I have?
I am using some analytics tools in the product, and for analytics, it is easy to do the data manipulation and easy to do the data blending. I have not used data visualization very much in KNIME Business Hub. My overall review rating for KNIME Business Hub is 9.
NataliaRaffo
Workflow automation has accelerated advanced analytics and machine learning delivery
Reviewed on Mar 31, 2026
Review from a verified AWS customer
What is our primary use case?
I am currently using KNIME Business Hub. In my experience, using KNIME Business Hub as a unified platform for developing advanced analytics and artificial intelligence solutions enables distributed processing of large-scale data through Spark. Implementation of modern lakehouse architectures that integrate data engineering, data science, and analytics within a single environment enhances scalability, model versioning, and team collaboration. Currently, I use KNIME Business Hub to build data pipelines, train models, and deploy analytical solutions into production environments.
I am also using other tools because my company has many clients and our clients have different tools. We need to construct the analytical solutions in these tools. For example, I am using Python because in Python we construct the statistical and analytical models. Python is the primary language for developing advanced analytics and artificial intelligence solutions, including machine learning, deep learning, and large-scale data processing. My company has strong experience with different libraries, such as Pandas, NumPy, Scikit-learn, and TensorFlow. For our clients, we need to build, validate, and optimize predictive models. My team is multidisciplinary, and we integrate solutions into production environments through APIs, process automation, and end-to-end analytical pipelines, ensuring scalability and maintainability of the models. I always use Python as well. However, I use KNIME Business Hub in the same way because KNIME Business Hub is very important for constructing advanced analytical models. KNIME Business Hub now has many nodes to use for big data, data quality, data governance, and advanced analytics. We use KNIME Business Hub as well. It depends on the client because we always try to analyze what tool our client has, and then we try to use this tool. KNIME Business Hub is another tool that we now use, and we use the Python nodes as well for advanced analytics. In data governance, we try to use KNIME Business Hub to construct the data quality rules and other analysis. For example, to assess and understand the maturity of the companies, we sometimes use KNIME Business Hub. I use different tools, but sometimes KNIME Business Hub, and other times Python and KNIME Business Hub are different tools. I also use Amazon Web Service and Azure.
My experience using KNIME Business Hub for the development of advanced analytics and machine learning solutions leverages a wide range of nodes across data preparation, modeling, and deployment stages. I always try to use specific nodes because we always try to use the CRISP-DM methodology, so we need to always do data preparation and transformation for advanced analytics solutions. Key nodes and components used include data preparation and transformation nodes such as File Reader, Row Filter, Column Filter, Missing Value, String Manipulation, Math Formula, Joiner, GroupBy, Pivoting, and Rule Engine. I use nodes for feature engineering, such as Normalizer, One to Many, Binner, Lag Column, and Feature Selection Loop, and other nodes for machine learning and AI. For example, Partitioning, Decision Tree Learner, Predictor, and Random Forest Learner are all models that KNIME Business Hub has, and we use them for our models. Sometimes, I always try to use the Python and R nodes because there I can program the code as well. For model evaluation, I use other nodes, such as Scorer, Confusion Matrix, and Numeric Scorer. I love KNIME Business Hub because I can construct workflow automation and deployment. For me, it is very clear to understand the process for constructing analytical and advanced statistical models. It is good for me to use KNIME Business Hub for that. I use KNIME Business Hub end-to-end, from data preparation and feature engineering to machine learning, model evaluation, and workflow automation, integrating Python and R when more advanced modeling is required. I always try to use KNIME Business Hub.
What is most valuable?
It is very important that I have the workflow automation integrated with Python nodes, for example, and I can construct our main code to construct the solutions. For us, it is very important to have the workflow automation. In KNIME Business Hub, it is possible because we have the end-to-end approach to the models. We have, for example, some nodes for data preparation, and other nodes for feature engineering, and other nodes for machine learning and model evaluation, for example. We have only one workflow with all the nodes and all the processes. For us, this is an important impact because, for example, we have to construct segmentation models for our customers, and we define a frequency to run the models. For example, we need to run the cluster segmentation around each month. We have the automation of the workflow and we need only to put a run in a button and the process runs. For us, this is an important impact because the time to obtain the results is very quick.
What needs improvement?
Sometimes it is a little bit difficult to use some nodes when we have many large-scale data, for example, CSV files with a large amount of data. It is sometimes difficult to try to import the data in KNIME Business Hub nodes because I think that some features that are in the CSV in text, for example, large text, is difficult for KNIME Business Hub to import these fields. I don't know why, but it is very difficult. We need to try to use different nodes for importing the data, such as File Reader and CSV Reader. However, I think that it is always the features that have much text, it is difficult for KNIME Business Hub to understand and import this information. I don't know why, or maybe I don't know if we don't know what the better option is to configure the node to import all the CSV or the data set. However, we have always had this problem. In some nodes, sometimes it is the same because sometimes, for example, I have a CSV and in my CSV, I have a feature that is, for example, a date. When I import this data set in the File Reader node, I have problems with this field because it is a date, but the problem is that it imports it as text, for example. We try to use their nodes that convert text to date, but sometimes it is difficult, and it is not immediate to transform the text into a date. So we needed to convert the text into a date in the CSV, and then import it again in the KNIME Business Hub node and try to have a good read of this field. I know that KNIME Business Hub has some nodes to convert text to date and others, but sometimes it is difficult to use these nodes. I don't know why. Maybe it needs a specific format for the date and we need to transform our feature in this option. So sometimes it is a large process to convert these features. However, sometimes we need to investigate and search for other nodes, and try with other nodes to import these cases.
For how long have I used the solution?
I started with KNIME Business Hub around fifteen years ago.
What do I think about the stability of the solution?
For me, it is great. I think that sometimes we have some missing problems in some nodes when we are constructing the statistical models, but we always try to visit the forum for KNIME Business Hub and then we try to resolve the problem. However, I think that for now, I need to come back again to Germany to make another training because I saw that KNIME Business Hub now has many new nodes and I need to explore the new nodes and try to use more. For now, KNIME Business Hub is excellent for me and for our team.
Which other solutions did I evaluate?
We are a partner from KNIME Business Hub at this moment and I made different certifications in Germany, in Berlin, with KNIME Business Hub about machine learning nodes. I think that was around 2016. In 2018, we made two certifications with KNIME Business Hub.
What other advice do I have?
For now, we always try to use KNIME Business Hub to integrate with Power BI because we use Power BI to present the results and the visualization for the models. In KNIME Business Hub, I try to use some graphics, but for our internal analysis. For our clients, we use Power BI to present the results for the models.
I think that KNIME Business Hub is very robust and is a leading solution for analytics and advanced analytics. I think that now we have many nodes to construct the analytical models in the big data nodes and to process structured data. This is important because it is very easy to use the nodes in KNIME Business Hub in these cases. For example, in Python, it is a little bit complex to construct the code. In KNIME Business Hub, we have the end-to-end approach to the workflow, the complete workflow to resolve the process for the model. This is very good to have good results and quick results for advanced solutions, for analytics and for artificial intelligence. I think that I prefer KNIME Business Hub to Python, for example.
I think that the price is good. I think that a good option is to analyze, for example, the cost for Amazon Web Service, AI components of Azure and Amazon, and try to compare to KNIME Business Hub, and I think that it is a good price. However, always in our solutions, we need to make a good calculation for all the solutions because we have many solutions, and because all our clients don't have KNIME Business Hub. Sometimes we use KNIME Business Hub for our internal development of the analytical models. However, sometimes our clients have KNIME Business Hub, so it is perfect because we can construct the models there. When our clients don't have KNIME Business Hub, we need to use other tools because sometimes our clients tell us that they need us to construct the model only in their tool, for example, Amazon Web Service or in Python, so we need to construct there. Because sometimes they don't know about KNIME Business Hub and they want to use the tools that they have. However, I think that it is comfortable to use KNIME Business Hub for our clients. They like it very much because it is very easy and now it is very robust for statistical and advanced analytical solutions. My overall rating for KNIME Business Hub is eight out of ten.
DanieleGentile
Enables fast project development with efficient workflow modifications and promising features while offering modularity and reusability
Reviewed on Apr 02, 2025
Review provided by PeerSpot
What is our primary use case?
I primarily use KNIME for ETL, extracting data from different sources. I extract data from endpoints of Drupal created for me by developers, then transfer this data into Oracle. After extracting, I create a model in Oracle with ETL, which is used by Power BI. Following this, I create a star schema of the data.
What is most valuable?
KNIME is simple and allows for fast project development due to its reusability. I appreciate the ability to make improvements or modifications in existing workflows. Although I have not yet used the forecasting and customer profiling features, I find them promising.
Another effective feature is the ability to use GET request objects to retrieve data from websites or APIs. This makes iterative steps easy to manage. It is more elastic and modern compared to SAP Data Services, allowing node creation and regrouping components or steps for reuse in different projects.
What needs improvement?
I have seen the potential to interact with Python, which is currently a bit limited. I am interested in the newer version, 5.4, when it becomes available. The machine learning and profileration aspects are fascinating and align with my academic background in statistics.
For how long have I used the solution?
I have been working with KNIME for almost five years now.
What do I think about the stability of the solution?
Occasionally, when using the GET object, there might be issues due to the velocity of the lines or the IT system of the commission. Overall, stability is not a significant concern.
What do I think about the scalability of the solution?
I have not encountered any scalability limitations with KNIME at the moment.
How are customer service and support?
I contacted their technical support around five times. While they cannot always provide immediate answers, they are generally efficient and simplify tasks, especially in the initial phase of learning KNIME.
Which solution did I use previously and why did I switch?
I also worked with Power BI and BusinessObjects and have experience with typical data services access.
How was the initial setup?
The initial setup was straightforward, taking between half an hour and an hour depending on the data entity.
Which other solutions did I evaluate?
I use SAP Data Services as well, but I find KNIME more elastic and modern.
What other advice do I have?
I am impressed by the modularity and reusability in KNIME, especially the ability to make small adjustments to object configurations. I am interested in its interaction with Python and machine learning aspects. Also, I recommend KNIME to others as I face difficulty finding reasons not to. My overall rating for KNIME is between nine and ten.
Ana Toftigues
Intuitive design and helps with academic work while graphic features need clarity
Reviewed on Dec 17, 2024
Review provided by PeerSpot
What is our primary use case?
I use KNIME for my academic works.
What is most valuable?
KNIME is more intuitive and easier to use, which is the principal advantage.
What needs improvement?
For graphics, the interface is a little confusing. So, this is a point that could be improved.
For how long have I used the solution?
I have been using KNIME for six months.
What other advice do I have?
I'd rate the solution seven out of ten.
reviewer1515237
Provides data analytics with easy setup and vast documentation
Reviewed on Jul 30, 2024
Review provided by PeerSpot
What is our primary use case?
We use the solution for data analytics and logic design.
How has it helped my organization?
The product is working fine with Oracle.
What needs improvement?
It is is written in Java. If they can output the Javascript, it will be much better. Also, it could be integrated with Visual Studio.
For how long have I used the solution?
I have been using KNIME for three years.
What do I think about the scalability of the solution?
20 users are using this solution. Scalability is quite easy, but handling many notes can become messy.
How are customer service and support?
Most of the things is available in the community.
How was the initial setup?
It's quite easy to setup.
I have a CSV reader. When I reset that CS reader, and It gave some error.
What other advice do I have?
I have a CSV reader, and I encounter an error whenever I try to save. However, if I reset the CSV reader, I am able to save successfully. It’s a rare issue, but there's something wrong with the CSV reader. The error message doesn't provide a solution, only indicating a problem with the CSV reader.
I want to save the project but always face saving issues. If I reset the node, the saving works fine. The error message isn’t clear about what is wrong or how to fix it. I discovered on my own that resetting the CSV reader from green to yellow allows me to save the project. This issue is quite rare.
Last Friday, there was a widespread CrowdStrike issue, and I had to restart my computer. After restarting, I lost my entire project.
I recommend the solution.
Overall, I rate the solution a nine out of ten.
Júlio César Gomes Fonseca
User-friendly tool with efficient integration features
Reviewed on Jul 15, 2024
Review provided by PeerSpot
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I extensively used KNIME for about one year and at least two months.
What do I think about the stability of the solution?
The product is quite stable.
What do I think about the scalability of the solution?
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.
How are customer service and support?
The community support is good, and plenty of shared knowledge is available.
Which solution did I use previously and why did I switch?
We had licensing issues with other tools, but KNIME worked well as an alternative.
What other advice do I have?
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.