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KNIME Analytics Platform

KNIME Analytics Platform

Overview

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.

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Recent Reviews

TrustRadius Insights

KNIME Analytics Platform has proven to be a valuable tool for a wide range of users and industries. Novice data scientists appreciate the …
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Value for money!

10 out of 10
November 05, 2023
Internal Audit needs to identify the exceptions in the data to address the risks. These risks could be coming from IT or Business. So, we …
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Empowering People

9 out of 10
October 20, 2023
Incentivized
We use KNIME for three overlapping use cases. (1) With its drag and drop interface and visual management of software code it is a great …
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Popular Features

View all 16 features
  • Connect to Multiple Data Sources (19)
    9.6
    96%
  • Data Transformations (19)
    9.4
    94%
  • Interactive Data Cleaning and Enrichment (19)
    9.0
    90%
  • Automatic Data Format Detection (19)
    9.0
    90%

Reviewer Pros & Cons

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Pricing

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KNIME Community Hub - Individual

$0

On Premise

KNIME Community Hub - Team

From €250

On Premise
per month Starts from 3 users

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visithttps://www.knime.com/knime…

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services
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Product Demos

Break into Deep Learning for Image Data without Code

YouTube

Automating Financial Calculations with KNIME

YouTube

Leveraging ChatGPT in KNIME workflows

YouTube

Best Practices to Build KNIME Workflows

YouTube

Automating Out of Spreadsheet Hell with KNIME

YouTube

KNIME Software Short Demo

knime.wistia.com
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Features

Platform Connectivity

Ability to connect to a wide variety of data sources

9.1
Avg 8.5

Data Exploration

Ability to explore data and develop insights

8
Avg 8.4

Data Preparation

Ability to prepare data for analysis

8.3
Avg 8.2

Platform Data Modeling

Building predictive data models

7.9
Avg 8.5

Model Deployment

Tools for deploying models into production

7.3
Avg 8.6
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Product Details

What is KNIME Analytics Platform?

KNIME empowers data users to build, collaborate, and upskill on data science. KNIME offers support across the data science life cycle, from creating analytical models to deploying them and sharing insights across the enterprise.

Users of KNIME tend to wear one of four hats:

Data experts can accelerate time to insight, collaborate with other disciplines, and empower upskilling across business functions. KNIME lets them:
* Connect to any data, access any analytic technique, and the choice to code in any language
* Get to insights faster using a low-code/no-code interface
* Eliminate repetitive, manual work by creating reusable, automated workflows
* Save and share Python scripts, analytical models, or data processes for reuse
* Provide blueprints that non-experts can learn and upskill from independently
* Speed up learning by accessing workflow samples by KNIME community members and experts
* Validate models with performance metrics and carry out cross validation to guarantee model stability
* Automatically document each step of the analysis visually * Maintain models and fix mistakes more easily with version control, debugging, tracking, and auditing

Business & domain experts can access and blend data, perform advanced analyses, and deliver timely insights in a visual, interactive environment that eliminates the need to code. They can prep data faster and do deeper analyses because KNIME lets them:
* Connect to all data sources and access any file format in one visual environment.
* Transform data self-sufficiently in the same visual environment without IT dependency
* Use visual workflows from others as blueprints to get started faster
* Automate repetitive data tasks like data prep and reporting with reusable workflows
* Minimize the time to spot and fix errors with each step of the analysis clearly visible, and track changes with version control
* Access thousands of self-explanatory nodes to perform the actions needed on data
* Create workflows of any complexity by joining nodes together via drag and drop

End users can get insights with custom-built, interactive data apps without needing to know how to code or build analytical models. They can make faster, data-driven decisions with advanced analytics at their disposal because KNIME lets them:
* Interact with analyses of any complexity level with a data app UI
* Access data apps via the browser with a secure connection or shareable link
* Identify patterns with job-relevant data apps and provide feedback to improve the model
* Lower the barrier between them and data science teams, enhancing analytics output accuracy
* Choose to get insights from simple dashboards or complex, interactive visualizations
* Explore data and perform ad hoc analyses using interaction points within data apps
* Avoid vendor lock-in and adapt to changing business needs with an extensible platform

MLOps and IT teams use KNIME to securely deploy, manage, and scale with a single installation while ensuring enterprise-grade security and governance. The platform enables them to:
* Safely deploy and monitor models from one single place
* Ensure adherence to best practices
* Meet enterprise needs while ensuring data security and governance
* Securely productionization data science at scale


KNIME Analytics Platform Features

Platform Connectivity Features

  • Supported: Connect to Multiple Data Sources
  • Supported: Extend Existing Data Sources
  • Supported: Automatic Data Format Detection

Data Exploration Features

  • Supported: Visualization
  • Supported: Interactive Data Analysis

Data Preparation Features

  • Supported: Interactive Data Cleaning and Enrichment
  • Supported: Data Transformations

Platform Data Modeling Features

  • Supported: Multiple Model Development Languages and Tools
  • Supported: Automated Machine Learning
  • Supported: Single platform for multiple model development

Model Deployment Features

  • Supported: Flexible Model Publishing Options

KNIME Analytics Platform Screenshots

Screenshot of 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.Screenshot of 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.Screenshot of the KNIME user interface elements — workflow toolbar, node action bar, rename components and metanodes.Screenshot of 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.Screenshot of the status of a KNIME node, which shows whether it's configured, not configured, executed, or has an error.Screenshot of the KNIME node action bar, which can be used to configure, execute, cancel, reset, and - when available - open the view.Screenshot of 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 workflowScreenshot of 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.Screenshot of how to set a workflow coach preferences.Screenshot of replacing a node into the workflow editor via drag & drop.Screenshot of the annotation field of a node, which is helpful for explainability and documenting of a workflow.Screenshot of the annotation function, which is helpful for explainability and documenting of a workflow.Screenshot of 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.Screenshot of 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.Screenshot of 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.Screenshot of 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.Screenshot of the KNIME Collections view. Upskill users by providing selected workflows, nodes, and links about a specific, common topic.Screenshot of the KNIME Business Hub versioning. Track changes to workflows easily and in a transparent way.Screenshot of 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.Screenshot of 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.

KNIME Analytics Platform Videos

KNIME Analytics Platform Technical Details

Deployment TypesOn-premise
Operating SystemsWindows, Linux, Mac
Mobile ApplicationNo
Supported CountriesGlobal
Supported LanguagesEnglish

Frequently Asked Questions

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.

KNIME Analytics Platform starts at $0.

Alteryx, Dataiku, and Qlik Sense are common alternatives for KNIME Analytics Platform.

Reviewers rate Extend Existing Data Sources highest, with a score of 10.

The most common users of KNIME Analytics Platform are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(68)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

KNIME Analytics Platform has proven to be a valuable tool for a wide range of users and industries. Novice data scientists appreciate the platform's no-code environment, which allows them to focus on the methodology and intent of their analyses without getting bogged down by syntax errors. The intuitive visual node structure is particularly beneficial for non-technical clients who prefer a user-friendly interface over coding, enabling them to find solutions quickly. Experienced data scientists also find value in KNIME Analytics Platform, as it allows them to work in their preferred language using the R and Python nodes.

The platform's flexibility and ability to integrate well with other systems like SQLite and Python make it suitable for consulting work with financial institutions. KNIME Analytics Platform also provides self-documenting capabilities, eliminating the need for manual documentation tasks. Additionally, the platform offers access to AI machine learning tools that prove advantageous for data analysis and finding patterns. It is commonly employed in risk analytics and model development within the banking industry, facilitating univariate and multivariate analysis as well as determining the statistical significance of variables.

Beyond finance, KNIME Analytics Platform finds utility in advanced data analytics and AI experiments. It is used for data analysis in sourcing and sales areas, including running prediction models. The platform allows users to start with simple tasks and gradually increase analysis complexity, making it accessible for organizations of varying skill levels. By automating data preparation and transformation, KNIME Analytics Platform saves precious time in data analysis processes. It supports various data formats like JSON and XML, further enhancing its versatility.

Furthermore, the KNIME community boasts hundreds of add-on modules that provide existing solutions for similar tasks, making it easier to tackle complex projects. With support for both simple and complex analytics, including AI algorithms, the platform caters to diverse analytical needs across industries. For marketing purposes, KNIME Analytics Platform excels at crunching large sets of data by facilitating data manipulation, report creation, and running prediction models. Its efficacy has earned it a reputation as a best-of-breed analytics platform that can drive real business value from data.

Many users commend KNIME Analytics Platform for its shallow learning curve, enabling immediate efficiencies in management reporting. The platform integrates seamlessly with other open-source libraries, empowering users to leverage advanced analytics and AI capabilities. It serves as a bridge between multiple data sources, facilitating data cleansing and transformation. Consequently, KNIME Analytics Platform is widely used across organizations for ETL, data integration, advanced analytics, and customer segmentation.

Notably, KNIME Analytics Platform has emerged as a cost-effective alternative to Alteryx software for many functionalities. Its applications extend beyond data analysis and into internal audit, where it helps identify exceptions in data, generate reports, and prepare management dashboards. With its data science capabilities, KNIME Analytics Platform assists in investigating big data issues and automating processes.

The platform's drag and drop interface and visual management of software code allow users to quickly test concepts and build prototypes of data pipelines, machine learning solutions, and data apps. Fast access to and blending of data from various sources, including databases, APIs, and flat files, is made possible by KNIME Analytics Platform. The wide range of pre-built nodes covering machine learning algorithms, combined with Python integration and shared components, ensures that users have the tools they need to fill any gaps in their workflows.

As organizations strive to go beyond spreadsheets and traditional BI systems, KNIME Analytics Platform fills the gap by providing professional-level data processing and data science capabilities to anyone. It not only offers standalone solutions but also provides collaboration and automation features through the server solution, allowing users to automate tasks and make data apps accessible to anyone within the organization. Whether it's building data science pipelines, automating tasks, or creating self-service analytics platforms, KNIME Analytics Platform proves to be a versatile tool that meets various business needs.

From NLP-related tasks like information retrieval to addressing customer segmentation challenges in marketing departments, KNIME Analytics Platform has become an indispensable tool for organizations across domains. Its powerful capabilities for data transformation make it a robust choice for meeting various data transformation needs within organizations.

The ease of use and power of KNIME Analytics Platform have garnered praise from users who appreciate its ability to automate simple processes or develop complex solutions involving machine learning and data science. With its deep integration with other open-source libraries and its ability to handle large datasets effectively, KNIME Analytics Platform empowers users to drive innovation and extract valuable insights from their data.

Users commonly recommend the following when it comes to KNIME:

  1. Try KNIME for beginners in data analytics. Users suggest using KNIME as a starting point for those new to data analytics. They feel that it provides a good foundation and helps in better understanding data sets and features.

  2. Utilize KNIME for data cleansing. KNIME's drag and drop feature is often praised by users, who recommend it for data cleansing tasks. They find this feature beneficial and user-friendly.

  3. Consider switching to other analytics tools once proficient. Several reviewers recommend using KNIME as an open source software specifically for beginners in data analytics. However, they suggest transitioning to other tools like Alteryx or similar options once users have gained proficiency with KNIME.

Overall, users appreciate KNIME's suitability for beginners in data analytics, its effectiveness in data ingestion and experimentation, as well as its role in disseminating knowledge within a company. They also mention the importance of mastering R/Python for effective implementation and note that while KNIME has a lot of examples to learn from, it does not replace regular reporting tools.

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