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KNIME Analytics Platform

Score7.8 out of 10

66 Reviews and Ratings

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What is KNIME Analytics Platform?

KNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services.

Categories & Use Cases

Media

the KNIME Modern UI. This is the the new user interface for the KNIME Analytics Platform that is available with improved look and feel as the default interface, from KNIME Analytics Platform version 5.1.0 release.
the KNIME Analytics Platform user interface - the KNIME Workbench - displays the current, open workflow(s). Here is the general user interface layout — application tabs, side panel, workflow editor and node monitor.
the KNIME user interface elements — workflow toolbar, node action bar, rename components and metanodes.
the entry page, which is displayed by clicking the Home tab. From here users can; check out example workflows to get started, access a local workspace, or even start a new workflow by clicking the yellow plus button.
the status of a KNIME node, which shows whether it's configured, not configured, executed, or has an error.
the KNIME node action bar, which can be used to configure, execute, cancel, reset, and - when available - open the view.
the common node port types. Nodes can have multiple input ports and multiple output ports. A collection of interconnected nodes, using the input ports on the left and output ports on the right, constitutes a workflow
the three ways nodes can be added to the workflow canvas; drag & drop, double click on the node in the node repository, or drop a connection into an empty area to display the quick nodes adding panel.
how to set a workflow coach preferences.
replacing a node into the workflow editor via drag & drop.
the annotation field of a node, which is helpful for explainability and documenting of a workflow.
the annotation function, which is helpful for explainability and documenting of a workflow.
the space explorer, which is where users can manage workflows, folders, components, and files in a space, either local or remote on a KNIME Hub instance.
the node repository, which is where currently installed nodes are available. Here, users can search for and then add a node from the repository into the workflow editor by drag & drop.
the node monitor. This is located on the bottom part of the workbench and is especially useful to inspect intermediate output tables in the workflow.
the KNIME Business Hub teams view. Resources can be owned by a team (e.g. spaces & the contained workflows, files, or components) so that team members can access these resources.
the KNIME Collections view. Upskill users by providing selected workflows, nodes, and links about a specific, common topic.
the KNIME Business Hub versioning. Track changes to workflows easily and in a transparent way.
the KNIME Business Hub deployment options. After a workflow is uploaded to KNIME Hub different type of deployments can be created. For example: a Data App, schedule, API service, or trigger.
the KNIME Business Hub Data Apps Portal. This page is available to every registered user. Consumers, for example, can access to this page to see all the data apps that have been shared with them, execute them at any time, interact with the workflow via a user interface, without the need to build a workflow or even know what happens under the hood.

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Key Features

Learn about the best (and worst!) features KNIME Analytics Platform has to offer, as determined by TrustRadius' reviewers.
Based on 66 ratings of KNIME Analytics Platform's features
View all features

Top Performing Features

  • Extend Existing Data Sources

    Use R or Python to create custom connectors for any APIs or databases

    Category average: 8.9

  • Connect to Multiple Data Sources

    Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion

    Category average: 8.8

  • Data Transformations

    Use visual tools for standard transformations

    Category average: 9.1

Areas for Improvement

  • Built-in Processors

    Library of processors for data quality checks

    Category average: 9

  • Security, Governance, and Cost Controls

    Built-in controls to mitigate compliance and audit risk with user activity tracking

    Category average: 8.6

  • Self-Service Model Delivery

    Multiple model delivery modes to comply with existing workflows

    Category average: 8.3

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