KNIME, a data analytics platform that enables data processing, interpretation, visualization, and reporting through connections between nodes under the Node Repository.
KNIME, short for Konstanz Information Miner (Konstanz Information Miner), is developed open source by a company with the same name1. KNIME is written in Java and built on the Eclipse platform. It is well-suited for big data processes that are constrained by existing fixed disk storage. It features an extension mechanism that allows for seamless integration with other systems; for more details, refer to the section on KNIME Extensions below. It is primarily used in data analysis applications for CRM [Customer Relationship Management] and business intelligence [business analytics] processes.
KNIME Node Structure Usage
KNIME features a workflow structure composed of modules, providing a wide range of components tailored to machine learning and data mining requirements. These components are referred to as “nodes” within the application2. Detailed operations can be performed without writing code, using connected nodes. These nodes are executed in the order of the workflow, and their outputs can be monitored via the console. Additionally, individual outputs from each node can be viewed separately.
The KNIME node structure is extensible and can be customized according to specific needs. Using existing node structures, KNIME can easily integrate with other data analytics and machine learning tools such as Weka,, Tableau, and RapidMiner. It supports the ARFF format and enables the use of code written in C, C++, R, Python, Java, and JavaScript.
The KNIME Analytics Platform and KNIME Server products may be preferred depending on your needs3. You can access the application and SDK download links for Windows, Linux, and macOS from the Downloads section on the website. Of course, I personally recommend downloading via a package manager. For macOS, I’m also sharing the command you can use with the Homebrew package manager:
brew cask install knimeAfter installation, when you launch the application, it will prompt you to define a workspace. You can later modify this workspace as needed. Let’s now see how you can download the development packages that match your requirements.
KNIME Extensions
The KNIME Analytics Platform includes hundreds of modules for data integration (file input/output, database nodes supporting all common database management systems via JDBC), data transformation (filters, transformers, joiners), and data analysis and visualization. Practical report templates in document formats such as doc, ppt, xls, and pdf can be created using KNIME. In addition to its core features, you can access a wide range of development and customization packages—including R language4, Weka, and Tableau—through the menu under Help > Install New Software… > in the Available Software section. Within this section, select the –All Available Sites– option to view a list of available packages. After selecting the relevant packages, clicking the Next button will initiate the installation and integration process.
After the configuration process, I recommend reviewing the Learning and other resources for various essential solutions and training programs such as Customer Intelligence, Social Media, Finance, and Cross Industry.
*[CRM]: Customer Relationship Management
*[SDK]: Software Development Kit