KNIME Reviews

I use KNIME for a wide variety of purposes. Most recently, I employed it in a survey and experiment to explore different teaching methods, specifically using software to help nurses learn drug dose calculations. This became particularly important during the pandemic when there was an urgent need to reskill healthcare professionals rapidly.

During the early days of the pandemic, no one knew how to treat COVID-19, and it was a dire situation with high mortality rates. The healthcare system had to adapt quickly, with retired nurses and doctors being called back into service and nursing students stepping up to meet the demand. A program I had previously worked on, which taught drug dose calculations, became crucial again. Nurses had to familiarize themselves with a whole new set of drugs.

I analyzed the data from this program using KNIME. I handled various statistical analyses, including mean, standard deviations, regression, correlation, and Wilcoxon signed-rank tests.

It's difficult to pinpoint one single feature because KNIME has so many. For starters, it's very easy to learn. You can get started with just a one-hour video. The drag-and-drop interface makes it user-friendly. For example, if you want to read an Excel file, drag the "read Excel file" node from the repository, configure it by specifying the file location, and run it. This gives you a table with all your data.

Next, you can clean the data by handling missing values, selecting specific columns you want to analyze, and then proceeding with your analysis, such as regression or correlation. KNIME has over 4,500 nodes available for download. 

In addition, KNIME offers various extensions. For instance, the text processing extension allows you to process text data efficiently. It's more powerful than other tools like NVivo and Palantir.

KNIME also has AI capabilities. If you're unsure about the next step, the AI assistant can suggest the most frequently used nodes based on your previous work.

Another valuable feature is the integration with Python. If you need to perform a task that requires Python, you can simply add a Python node, write the necessary code,

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.

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.

KNIME is an analytics platform. We use the tool for prototyping solutions by using flat files without database connections. We use it for the decision tree models.

The product is user-friendly. It is easy to use. My experience is very good. We could see the benefits of the solution immediately after deployment. It is a complete solution. The documentation is very good.

Rather than specific use cases, I've used it in different sectors. Mostly education. For example, we've used it to predict the kinds of courses or degrees students should pursue based on their skill set and learning capability.

KNIME is very easy to handle and use. Anyone can use it, and it's easy to learn. You don't need a specific class. They're very good at model prediction. It has got everything.

From data analysis to model prediction and application, it's very good. I only use the free community edition, not the enterprise one. I feel KNIME is really good. I haven't tried any other tool or platform yet, but KNIME is pretty good.

The workflow is great. You drag and drop, and then you have the data explorer and charts that give results. 

The execution is also good – it's easy to identify where your model has gone wrong. It shows you the exact point of error within the workflow, so you don't have to execute the entire workflow to find it. For example, if your workflow has ten steps and the error is in the sixth step, it will show you the error at that step. You don't have to worry about the first five steps.

The Data Explorer is very good, and the charts are great too. The accuracy charts for different models, like decision tree, K3, Naive Bayes, are all very good. KNIME is great at reporting, whether it's structured or unstructured data. These are all very good features.

It is suitable for various industries, including government, enterprise business, product manufacturing, and banking and finance. KNIME provides effective solutions for data-related tasks.

It takes and improves performance analytics.

KNIME is an excellent product, and I've used many other platforms like Google Collab, Azure, and even AWS. However, KNIME, especially for AI and machine learning, is very different. It's almost no-code. You can add code if needed, but it's not necessary.

KNIME has hundreds, maybe even thousands of modules, which are called nodes. These nodes, along with their libraries, are essential for solving specific issues or problems. You can select the nodes you need, and they come pre-recorded as visual boxes. You just need to assemble the nodes required for your solution. As mentioned earlier, you can search for libraries and select the appropriate nodes, then combine them to form your entire workflow. KNIME supports coding in Python and other languages, but you can assemble the nodes visually without writing code. Each node has a specific function, and if one node doesn't suit your needs, you can easily replace it with a different one.

Additionally, each node has inputs and outputs, and you can configure them based on your requirements. Once the nodes are set up, you can attach the data and let it flow through the nodes to execute your workflow.

One significant improvement is its speed. With KNIME, you can accomplish many tasks in a single day. It's very fast since you mostly work with prebuilt nodes and libraries. Also, the latest version allows us to add Python code if needed.

We use KNIME for tax technology. We want to implement technology in our tax domain.

KNIME is very fast and scalable. There are a lot of connectors available in KNIME.

I use KNIME to simplify the modeling process. 

The tool's analytic capabilities are good.