KNIME Analytics Platform

Score7.8 out of 10

66 Reviews and Ratings

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

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 workflow

Screenshot 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.

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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.

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

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

Rob Blanford View profile

Artificial Intelligence & Machine Learning Manager in Information Technology at Eviden (10,001+ employees employees)

Use Cases and Deployment Scope

The KNIME Analytics Platform provides a comprehensive set of tools for addressing the data manipulation and data science issues we encounter. Internally we use it for training new data scientists, building awareness of the data science workflow and data manipulation with non-technical staff. We also use it on our own data projects. The no-code environment allows us to focus on the methodology and intent of analyses with novice users without them encountering errors in syntax as they would if they were learning to code at the same time. However, the R and Python nodes allow experienced data scientists to work in their preferred language as well as allowing us to scaffold the learning of new data scientists in those languages when it becomes advantageous. We find non-technical clients will engage with the visual node structure much more than code, which helps us get to a solution more quickly. We can deliver stand-alone solutions to clients with confidence that we are not tying them into an expensive vendor relationship. Clients value that they can give access to all of their users at no licencing cost. Where collaboration and automation is required, KNIME Analytics Platform offer an extremely competitive server solution.

Pros

Connectivity to an array of data sources and joining the data
Rapid prototyping across data science use cases
Making data science explainable to non-experts
Democratising data - KNIME Analytics Platform allows everyone access to powerful analysis techniques
Providing simple access to powerful external data science tools such as H2O and hyperscalers

Cons

The previous UI of KNIME Analytics Platform provided easy access to a wide range of examples which is an extremely valuable resource for understanding how to break down a problem in KNIME Analytics Platform and provide accelerated delivery for similar use cases. Access to these resources doesn't seem possible at the moment in the new UI, but I believe it is being actively worked on. The examples are still available in the platform, but presently you need to switch back to the old UI.

Return on Investment

KNIME Analytics Platform is free and our investment in training time has been paid back many times over when using the software for rapid prototyping and implementing our own analysis
On one occasion we deployed KNIME Analytics Platform and Python with a client. Due to their secure environment, it was prohibitively expensive in time and capital to add Python packages to account for additional requirements as the project progressed and we switched entirely to KNIME Analytics Platform due it its comprehensive set of features. Without KNIME Analytics Platform the project would have been halted and resulted in a loss of >£40k in revenue.

Alternatives Considered

Alteryx, RapidMiner, Dataiku DSS, IBM SPSS Statistics and Anaconda

Other Software Used

Microsoft Power BI

Usability

Marc Cooley View profile

President in Professional Services at Kohl Analytics Group (1-10 employees employees)

Use Cases and Deployment Scope

KNIME Analytics Platform is the central data processing system we use in our consulting work with financial institutions. We have found the flexiaibity, integration with other systems such as SQLite, Python, etc. to be a great advantage. The self documenting nature of the GUI is awesome and eliminates a task no one wants to do (documentation). Access to AI machine learning tools is another bonus we are also exploiting to a great advantage.

Pros

Summarize instrument level financial data with relevant statistics
Map transactions from core extracts to groups of like transactions using rule engines
Machine learning using random forests and other techniques to analyze data and identify correlations for use in predictive models
Fill out sampling data from averages.

Cons

The Excel reader node doesn't always reset. Sometimes the node has to be rebuilt or reconfigured to truely reset the node. This can trip you up if you're not aware of it.
Basic filtering in table view. Sure you just add a filter node, but it would be cool if the data tables worked more like tables in Excel where you can filter as well as sort.

Return on Investment

Signficantly reduced training time for new anaysts. This is due to the self documenting nature of the GUI.
Improved turnaround time on standardized client work.
Reduced time spent in development since the focus can be on what is being done, not the syntax of the code.

Alternatives Considered

SQLite, Microsoft Excel and Microsoft Access

Other Software Used

Microsoft Office 365, HubSpot CRM, ClickUp, Google Drive, Adobe Acrobat

Mathias Denzin View profile

Senior Salesforce Solution Engineer in Information Technology at SMA Solar Technology America (1001-5000 employees employees)

Use Cases and Deployment Scope

KNIME Analytics Platform is the perfect tool for data loading, transformation, and analytics. The flexibility with Python and R scripting is incredible and replaces many manual data tasks. Every manual data transformation frequently done in Excel should be transformed into a KNIME Analytics Platform flow. In most cases, there is no need for manual steps in data analytics when using KNIME Analytics Platform. The available documentation and training are perfect for starters. The low-code-no-code approach gives unexperienced users a great chance to step up in the data analytics game. KNIME Analytics Platform is an everyday tool in my work life.

Pros

visual data flow creation
huge number of built-in nodes and function
very supportive community
webinars about use-cases and new functions

Cons

report design (a modern BIRT)

Return on Investment

I have no numbers, but I know how much time I save after I created a flow and rerun it without manual inputs and steps needed

Other Software Used

Microsoft Power BI, Tableau Desktop

Asam Salim MCMI ChMC View profile

Head of Analytics at Atos (10,001+ employees employees)

Use Cases and Deployment Scope

As Head of Analytics for Eviden (an Atos company), I have built a strong relationship with KNIME as I wholeheartedly believe in the democratisation of best of breed analytics platforms, of which KNIME is one, that can significantly drive organisations to real business value from their data. It has a shallow learning curve and can drive immediate efficiencies in a short space of time, particularly in the management reporting space for those that are report producers.
As the KNIME Analytics Platform is open source, it integrates with other open source libraries and can accelerate organisations to delve into advanced analytics and AI in areas of prediction for example. There isn't a better time than today to unleash this platform across your user base and reap the value of enhanced quality of insights in parallel to increasing data literacy.

Pros

Visual programming as oppose to scripting encourages data analysts to reap deeper insights from their data
Large community contribution in extending the KNIME Analytics Platform into other areas of analytics, e.g. Text Analytics, Predictive Analytics, ML, etc.
Open source with periodic updates ensures it is equipped to deal with the most sophisticated data analytics use case

Cons

User interface has recently been improved to align with good practice on UX

Return on Investment

High ROI
Time savings through report automation
the visual programming capability allows for wider team contribution from SMEs as they understand the construct of the analysis. Inclusivity from this perspective is key.

Other Software Used

Microsoft Excel, Microsoft Visual Studio Code, Microsoft Power BI

Mats Lagerqvist

Risk Manager at Nordax Bank (501-1000 employees employees)

Use Cases and Deployment Scope

Nordax Bank uses the KNIME Analytics Platform to build risk and marketing models.

Pros

Machine learning models
Great support and user examples
Format that allows users to build very flexible workstreams

Cons

An optimization module that allows users to define constraints

Other Software Used

IBM SPSS Statistics, Tableau Desktop

Return on Investment

Significantly reduced time from modeling to production
Increased sales with more accurate response models
Shallow learning curve and free access to web-based tutorials

KNIME Analytics Platform

What is KNIME Analytics Platform?

Categories & Use Cases

Media

Key Features

Top Performing Features

Extend Existing Data Sources

Connect to Multiple Data Sources

Data Transformations

Areas for Improvement

Built-in Processors

Security, Governance, and Cost Controls

Self-Service Model Delivery

Reviews

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Usability

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Other Software Used

Return on Investment

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