KNIME Reviews

It's mostly data preprocessing, handling, and processing (ETL) processes, as well as expanding the transport load. 

Additionally, we also work on various machine learning tasks, such as regression models and other small topics related to machine learning.

I've tried to utilize KNIME to the fullest extent possible to replace Excel. Our company has been heavily reliant on Excel for generating reports and performing data transformations. With KNIME, I've been able to combine data from Excel, SQL Server, and various other resources efficiently.

There are a few aspects that I am not entirely satisfied with. For instance, when integrating KNIME with our SAP system ERP and HANA, it's not as straightforward as expected. We need to find alternative connectors like the Teradata connector, which adds complexity.

So far, I've had some problems integrating KNIME with other solutions. Thus, it could be an area of improvement. 

We have been using KNIME for two years.

Overall, the product has been stable. It has efficiently handled the tasks we have encountered so far.

There are two end-users using KNIME in our organization. Because we are still beginners, we are only using it to learn how it works and get a better understanding of the system. We are not yet certain if we will use it extensively for all topics.

The initial setup was easy. 

I deployed the solution myself. 

We use the free version only. 

We are working with KNIME on some small projects, but we are also looking for an alternative solution to explore.

Overall, I would rate KNIME a seven out of ten because we faced a problem with the integration with other products, like SAP.

In demand forecasting projects to extract, to clean and to transform data from various resources. Also some clustering and classification techniques are used for behavioural clustering and classification according to attributes.

My organization's field of activity is to develop business applications for niche areas. Almost three years ago, we decided to extend our solutions with advanced analytics. KNIME let us start easy and fast into the Advanced Analytics area. We are able to try project ideas with KNIME by doing proof of concept easy and prototyping fast.

• Easy ETL operations
• Rich algorithm set
• Integrated with other languages like R, Python, and Java.
• Works together with other technologies like DeepLearning4j, H2O.ai, D3.js, and Weka.
• Ease of use and being a performant solution.
• Continues development and wide community support

I mentioned about the distributed architecture in my previous answer, but they did with version 3.5. This time maybe I could add the integration with graph databases like Neo4j.

In the previous versions, I had some issues when reading large Excel files due to memory usage. But with the previous version (3.3), they renewed all Excel nodes and now I have no problem. 

With the data sizes that I dealt with, I did not. Our average record size was around 10 million records. If we have bigger data, we can opt for a Big Data extension for Hadoop, Spark, etc.

I used it very little. All of them replied to me in one day. (It was not professional support, just over a forum). Also, I can find enough information in the documentation and forum.

Before KNIME, I used SQL language and Excel for data analysis but machine learning algorithms. In parallel to KNIME, I worked on a few projects with R and Python separately. So I cannot say that I switched from different solutions.

But just for ETL with Excel, KNIME brings me better visualization, rich function set, preserving operations to repeat again and better performance on the same hardware.

I am using Mac and it is so easy. Download a .dmg, extract it as an app, and copy it to the applications folder. On windows it is also simple installation.

For extensions like R or Python, you need experience with general OS and installation processes.

We did in-house.

The biggest ROI comes from productivity when creating new things and also supporting old jobs.

And there is no hidden cost. Licensing is simple and open than other platforms.

KNIME is open sourced platform and has a free desktop version with unlimited data size and functionality.

Also, the server version is good for teams and enterprise productivity. Especially the new "Model Factory", which lets data science teams easily build and manage models. When compared with similar products, it is less expensive but as powerful as (or maybe more powerful than) others.

The Open Source licensing and community support is one of our important criteria. The second one is the interoperability with other technologies and openness to different data sources. There are two options: RapidMiner and KNIME. We chose KNIME.

Data Science requires freedom for creativity. Sometimes you need to crawl data from the web or social media. Sometimes you need to blend different sources like NoSQL MongoDB and Excel files, etc. It is not only algorithms and data extraction, visualization and preparation steps are important as at least algorithms.

Don't go with software that has complex and hidden licensing costs, which will kill your flexibilty and creativity. Also, interoperability brings the advantage of limitlessness.

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.

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 primarily use the solution as an ETL - to Extract, Transform, Load topics.

All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function.

Technical support has been extremely responsive so far.

The solution has a very strong and supportive community that shares information and helps each other troubleshoot.

The solution is very stable.

The initial setup is pretty simple and straightforward.

The user interface could be a little bit more comfortable. The usability, in general, could be much better.

The predefined workflows could use a bit of improvement.

I've been using the solution for five years.

The solution is pretty stable. I don't recall facing any bugs or glitches. The solution doesn't crash or freeze. It's fairly reliable overall.

We have about 10 users in the company that are on the solution regularly. They are all either engineers or business consultants.

The technical support on offer is very good. We rarely have issues, however, if we do, the support that is provided is very fast and the team is very knowledgeable. There's also a very strong and active KNIME community that includes KNIME developers.

At the end of the day, when we run into issues, it's dealt with immediately and we're very satisfied with the level of service provided.

The initial setup isn't complex. I'd describe it as straightforward. What makes it very easy is that there's so much material available online. Users have access to many forms of documentation, including webinars and training documents. When it comes to finer details, of course, it gets more complex, however, that's pretty normal in every product. 

We are KNIME partners and also provide KNIME training to clients as well.

We're a startup, and we're in the startup phase, however, we are working to build more business relationships with KNIME.

In my previous company, we were also partnered with KNIME. Currently, I'm working as a consultant and we have a customer who is using SAP Analytics, however, I am interested in learning more about the differences between KNIME and SAP Analytics. That's something I'm looking at right now.

We would definitely recommend this solution to other organizations.

I'd rate the solution eight out of ten. I don't know if there's a tool better than this particular product, however, there's always room for improvement on any technology.

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. 

I encountered a problem that I managed to resolve effectively. I documented the issue in a paper and aimed to determine if the issue was due to normal network behavior or an anomaly. To investigate, I employed machine learning models and used the KNIME’s database. I gathered a significant amount of data and extensively applied machine learning models. Ultimately, I achieved improved data accuracy, especially in the context of network data.

I believe that some individuals may not be skilled programmers, and this is where the agenda comes in. It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea.   

The pricing needs improvement. 

I have been using KNIME for the past six months.

It is stable solution.

It is scalable solution. 

The initial setup is straightforward.

It is expensive to procure the license.

I would rate the overall solution an eight out of ten. I would suggest this application to individuals involved in auditing. It's user-friendly and makes it easy to initiate model creation.

I am an intern. I am pursuing my master’s degree. I use this solution to propose a solution for accreditation review. I needed a tool to automate this task for my sources. This solution has helped me to do that.

The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes. So maybe it would be helpful to have a way to find similar column names automatically and execute a single approval, for instance.

In future releases, I can suggest having an API along with tools or services. This would allow for customization since the API is already available in other versions.

So, I suggest having an API for customization.

I have been using KNIME for six months.

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

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

I have used Alteryx. I prefer this solution over Alteryx because of the pricing point. KNIME is free and open source. 

The initial setup is easy. I will rate my experience with the initial setup a nine out of ten, where one means it's difficult to set up, while ten means it's very easy.

I deployed the solution myself. It took around one month to deploy it. 

KNIME is free and open source. I would rate the pricing model one out of ten, where one is low price, and ten is high price. 

It is a good tool and easy to learn. We do not need to know the codes before using the solution.

Overall, I would rate the solution a seven out of ten.