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Importance of KNIME Analytics Platform

Why KNIME?

KNIME Analytics Platform is open-source software built for powerful analytics. It is free and easy with drag and drop based Graphical User Interface (GUI). KNIME is designed in such a way that the user need not have to know how to code to derive insights. Users can perform functions ranging from basic I/O to data operations, modifications and data mining. KNIME then combines all the functions of the complete process into a single workflow. After understanding why KNIME? now find out What is KNIME & Its Applications

Recommended Article: Top Features of KNIME Analytics Platform

Importance of KNIME

When KNIME was designed it was decided that the product would have to process and integrate vast amounts of varied data. Therefore, the developers followed the demanding software engineering standards to create a strong, integrated, and highly scalable platform surrounding various data loading, transformation, analysis, and visual exploration models. The first version of KNIME was released in 2006 when many pharmaceutical companies started using it and, subsequently, software vendors started developing KNIME-based tools.

Now, KNIME users can be found in huge-scale enterprises across a varied range of industries like life sciences, financial services, publishers, Retailers, manufacturing & consulting firms, government and research all over the world.

KNIME is written in Java and it is open-source multi-language software that includes an integrated development environment (IDE) and an extensible plug-in system.

Must Check: Basic Building Blocks of Nodes & Workflow in KNIME

What’s so Great About KNIME?

KNIME provides a graphical user interface to chain together blocks that represent steps in a data science workflow. It has dozens of built-in data access and transformation functions, statistical inference, and machine learning algorithms, PMML, and custom Python, Java, R, Scala, a zillion other nodes, or other community plugins. Even better, KNIME imposes structure and modularity on a data science workflow by requiring code fit into specified building blocks.

For non-coders, there are premade analytical blocks that have an input port for the tables you will use, and an output port to send the result to a dashboard or other destination. There’s also strong PMML support, so if the complex models are built in another enterprise tool with PMML support, they can be exported and used in KNIME.

And if there are programmers who want to use Python or R, they can develop in their preferred language and still interface well with everyone else using KNIME’s building blocks. This is because the blocks force users to build composable modules by imposing variable names and types for each block’s input and output ports.

With KNIME imposing modularity via its user interface, it becomes easier to share parts of workflow across projects so that the analysts can do their job knowing they have complete inputs. You can learn more from Our Latest high-quality KNIME Online Training with Certification Program.