The KNIME system can operate on multiple operating systems. In other words, it is compatible with the Windows 32bit and Windows 64bit version and stays updated with Vista and all machines under Windows 7. It can also function with numerous Linux systems like RHEL4/5 and Open SUSE 10.2/10.3/11.0. KNIME version 2.1 can also work on macOS. Before knowing the exact features of the KNIME Analytics Platform, first, know the What is KNIME & its Importance.
KNIME is loaded with full of Features of KNIME. To practice the KNIME Analytics Platform, you have to first download and install the KNIME Tool on your windows or mac.
What is KNIME?
Scalability is one of the key features that KNIME offers. With its innumerable extensions, there are many ways to modify and raise the system to fit the company’s specific needs. The in-built user interface also helps speed up the learning curve. In the KNIME system, the interface makes everything quite easy to use. KNIME users can import and export workflows easily.
For an environment that runs a multi-core system, features like parallel execution are extremely valuable. Along with this KNIME also has the capability of “headless” batch executions using the command line version and that’s the reason why many prefer KNIME.
Must Check:Importance of KNIME Analytics Platform
Before going in deep explanation just take an idea about the Main features of KNIME Analytics Platform are:
- Big Data Extensions
- Data Blending
- Tool Blending
- Meta Node Linking
- Local Automation
- Workflow Difference
- Data Manipulation
- Data Mining
Features of KNIME Analytics Platform
So here some important features of the KNIME Analytics Platform as follows:
- Big Data Extensions- KNIME Big Data Extensions integrate the power of Apache Hadoop and Apache Spark along with the KNIME Analytics Platform and KNIME Server. KNIME software takes the confusion out of big data by making it accessible within the familiar analytics environment.
- Data Blending- Data Blending in KNIME includes simple text files, databases, documents, images, networks, and even Hadoop-based data that can all be combined within the same visual workflow.
- Tool blending- Tool blending means integration of multiple tools like legacy scripting/code, allows expertise to be reused, graphically documented, and shared among data scientists.
- Meta node linking- Meta nodes are gray nodes that comprise of sub-workflows. They are used as functions or macros in the script-based tools.
- Local automation- Local Automation allows the call workflow nodes to be involved in any workflow. This allows the formation of reusable workflows, adding another layer of flexibility to your toolkit.
- Workflow difference- Manual identification and comparison of workflows can be time-consuming and difficult. Workflow Difference automates this task by applying a matching algorithm to analyze and identify additions, removals, replacements, and limitation changes among nodes and workflows.
- Data Manipulation- It is another frequently used module. It can pre-process your data with the ability to filter, group, pivot, bin, normalize, aggregate, join, sample, partition, and so on.
- Data Mining- It makes use of multiple algorithms like clustering and neural networks to help the KNIME user better understand their data.
Check out the latest KNIME Online Course by Yoda Learning. This certification training course is designed for students and professionals, who want to become a KNIME Analyst/Expert. With this course, you can start learning from scratch and become pro after completion.