KNIME Verified Components

Easily reuse bundled functionalities, verified by KNIME experts.

KNIME Components
A set of Components that behave like KNIME nodes, including error handling capabilities, which are developed by KNIME and regularly released on the KNIME Hub. We'll regularly update this page with new Components - here are the most recent ones:

Overdue Invoices Status

This component is designed for automated control of overdue invoices. Based on the invoice issue date and payment terms, it calculates the due date, number of days until the due date, and the due status.

Category: Finance Analytics

Authors: Lada Rudnitckaia, Junior Data Scientist at KNIME and Felix Kergl-Räpple, Junior Business Analyst at KNIME

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KNIME Verified Component Overdue Invoices Status

Overdue Invoices Email Notification

This component is designed for automated reporting of overdue invoices. It automatically sends emails to managers about invoices that require some particular action.

Category: Finance Analytics

Authors: Lada Rudnitckaia, Junior Data Scientist at KNIME and Felix Kergl-Räpple, Junior Business Analyst at KNIME

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KNIME Verified Component Overdue Invoices Email Notification

Categories to Excel Sheets

This component creates an Excel file and writes the input table to the separate sheets of this file so that each sheet contains a subset of the table as defined by the categories in a selected column.

Category: Finance Analytics

Authors: Lada Rudnitckaia, Junior Data Scientist at KNIME and Felix Kergl-Räpple, Junior Business Analyst at KNIME

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KNIME Verified Component Categories to Excel Sheets

Browse Verified Components History.

Verified Component Categories

Verified components are officially released by the KNIME team and are divided into the following categories. Explore all the verified Components in each category on the KNIME Hub.

Automation

Automation Components help when a workflow has to be executed in a production environment in an automated fashion - from complex AutoML to simple tricks to increase flexibility and traceability of your workflow.

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Data Manipulation

When dealing with raw data, there are recurrent data manipulation techniques requiring complex workflows. We offer those workflows via Components to quickly clean, rename, filter, and transform raw data columns.

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Finance Analytics

Finance teams across all industries require specific techniques to keep track of the company’s numbers while ensuring precision and compliance. Those time-consuming tasks usually rely on data from different spreadsheets, which can be hard to maintain and update. These Components help build reusable workflows for an efficient financial analysis.

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knime_icons_rz Guided Analytics

Components can generate views when using Widget and JavaScript nodes. Those views can be used on their own or in sequence to interactively guide the user through the analysys. Use the Guided Analytics Components to create guided analytics workflows for local usage or remote access via KNIME WebPortal.

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Life Sciences

Gather and analyse Life Science data with shared Components. For example, extract data from the European Nucleotide Archive, ChEMBL or PDB or perform a Pathway Enrichment Analysis by simply dragging and dropping the Component of your choice.

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knime_icons_rz Model Interpretability

Training performant predictive models often leads to black boxes: data goes in the box, it's processed by nearly incomprehensible algorithms, predictions come out of the box. KNIME offers an extension to be combined with these Components for Machine Learning Interpretability (MLI) and Explainable AI (XAI) use cases.

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Text Processing

The text processing Components help you to analyse text documents - from extracting data from biomedical literature, to document preprocessing, through to computing document similarity.

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knime_icons_rz Time Series

Easily clean, aggregate, visualize, and forecast time series data with this set of Components. Includes options for seasonality visualization and removal, ACF and PACF plots, as well as ARIMA forecasting.

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Visualizations

Countless visualizations exist to display data in colorful charts. Most of those visualizations are already provided in KNIME via JavaScript and Plotly nodes. If you cannot find the plot you need as a standard node, make sure to check this category of Components with more complex visualizations or interactive composite views.

 

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Community Component Highlights

Components can also be shared by community members via the KNIME Hub. In this section, we promote the Community Components that have been downloaded the most. These are selected and reviewed by KNIME experts and each quarter shared in this section.

For Q1 2021 we’d like to highlight and recommend two Components shared by Armin Ghassemi Rudd, Data Science Instructor & Consultant (Freelance) - our Contributor of the Month for February!

KNIME Component Translator
This Community Component uses Google Translate API to translate any input text from/to supported languages.
KNIME Component Get Request Plus
This Community Component works in a similar way to the Get Request node as well as having the retry option. It only works with JSON content types by default. To change this behavior or add headers, the component should be modified.

Build your own Components and share them with the KNIME Community on the KNIME Hub!

KNIME Components Drag and Drop

What are Components?

Components are really KNIME nodes that you create with a KNIME workflow, enabling you to easily bundle, reuse, and share functionality. Configuration and widget nodes allow you to create Components that behave just like normal nodes with a logo, a dialogue, and often interactive views. With KNIME Analytics Platform, anyone can create Components and share them, via the KNIME Hub, with the community.

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Browse all Components

Visit the KNIME Hub to browse all available components and add them to your workflow.

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About KNIME Hub

Learn more about the KNIME Hub and how it can help with your data science solutions.

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Read the Blog

Learn more about the difference between components and metanodes in KNIME.

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