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QuickSight vs KNIME

Last Updated: November 18th, 2024

Our analysts compared QuickSight vs KNIME based on data from our 400+ point analysis of Business Intelligence Tools, user reviews and our own crowdsourced data from our free software selection platform.

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Product Basics

Amazon QuickSight is a business intelligence tool that enables organizations to visualize and analyze data, derive insights, and make informed decisions. It empowers users with interactive dashboards, data exploration capabilities, and machine learning-driven insights, making it an ideal solution for businesses seeking to leverage data for better outcomes.

The benefits of Amazon QuickSight include its ease of use, fast performance, and scalability to handle large datasets. Its popular features include natural language querying, automated insights generation, and integration with other AWS services. Compared to similar products, Amazon QuickSight stands out for its affordability, ease of deployment, and robust feature set.

Pricing for Amazon QuickSight is based on a per-user, per-month subscription model. It offers flexible pricing options to accommodate varying business needs, ensuring cost-effectiveness and scalability as organizations grow.

KNIME is an open-source end-to-end data analytics solution. It utilizes visual workflows with drag-and-drop functionality and thousands of nodes to lessen the data analytics learning curve data, with more than 1,800 prebuilt default workflows for streamlined setup.

It allows for data ingestion, preparing, cleansing, analyzing and visualizing. It can be scaled for deeper analytics through integrations with sophisticated data modeling capabilities. It can be hosted on-premise or in the cloud through Microsoft Azure.

$5/User, Monthly

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$0 Open-Source

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Product Insights

Self-Service BI: Get answers to questions and share insights independently through self-service analysis of business data. Connect to data sources, manipulate values to create visualizations, invite others to collaborate and share dashboards and reports — all without the need to install any application.
Multi-Tenancy: Its multi-tenant architecture with namespaces allows vendors to provide data exploration and dashboarding capabilities for multiple applications at the same time. Host multiple clients and add templates created and shared by dashboard authors to the proprietary visualizations library.
Embedded Analytics: Embed dashboard sessions into proprietary software to enhance business with console analytics. Personalize the appearance of visualizations and reports by adding custom fonts, colors and styles. Set defaults and error handling to match the host application for a cohesive feel.
Report Sharing: Stay informed as to how key metrics are performing by scheduling automatic email updates. Ensure secure access to dashboards and email reports by showing only relevant data through targeted reports.
Flexible Pricing: Save on upfront costs and capacity planning through its p ay-as-you-go pricing model for report consumers — people who view dashboards, but don’t create them.
Mobility: Get data insight and forecasting summaries on-the-go through its mobile version, available on both iOS and Android. Drill down into mobile-optimized dashboards and reports for in-depth analysis and filter to view specific metrics, even when away from the office. Share results with others and get email alerts for changes in business data as they happen.
Automate Deployments: Deploy applications at scale, keeping them online for continued access while minimizing downtime. AWS CodeDeploy provides automated app deployments to multiple cloud compute services, such as Amazon EC2, AWS Fargate, AWS Lambda, and on-premise servers. Seamlessly integrates with continuous delivery process pipelines, such as AWS CodePipeline, GitHub and Jenkins.
Promote Data Literacy: Identify key metrics, perform what-if analysis through simple point-and-click actions, discover hidden trends and generate forecasts — without needing any technical skills. Generates dashboard summaries in easy-to-understand language to enable shared understanding of data insights.
Scalable: Its serverless architecture makes it easily scalable, ensuring fast loading of dashboards even during peak traffic. Scales as the organization grows to take on larger loads — no need to set up dedicated servers.
Data Security: Its Enterprise version provides additional security features through permissions-based access to AWS data, row level security, SSO and data encryption. It is FedRamp, HIPAA, PCI PSS, ISO and SOC compliant, keeping with industry best practices.

Open-Source: Join a network of thousands of users, enabling collaboration and support. The source code is free to download and access.
Free To Use: Save money by getting access to all of the platform’s features for free. Licensed productivity and collaboration extensions are available at a cost.
Increased Business Intelligence: Get digestible, actionable data to make informed business decisions. Aggregating large datasets into predictive and prescriptive models via comprehensive visualizations and summary statistics gives users projections for the best course of action.
Scalable: Obtain access to big data by scaling up the project in-platform. Integrations to distributed and multi-threaded data processing allow projects to grow.
End-To-End Analytics: It is capable of handling some tasks from start to finish without integrations. Additional integrations may be required for increasing scale and completing more sophisticated analytics.

Data Connectivity: Connect to a multitude of cloud-based and on-premise data sources such as Salesforce, JIRA, Github, Teradata, MySQL, SQL Server, Postgres and many more, or upload data from Excel, JSON, CSV and Presto files.
Integrations: Perform predictive data analysis by integrating Amazon Sagemaker ML-based data models with its supported data sources, such as S3, Athena, Redshift, as well as third party sources that include Salesforce and JIRA, among others. Easily integrates with BI tools such as Domo and Tableau for data querying through an intuitively similar SQL interface.
Data Visualization: Create interactive visualizations and reports by choosing from a rich library of graphs, charts and tables. Build dynamic dashboards by interacting with data in real time through drag-and-drop, drill-down and filtering options.
Reporting: Its Enterprise edition provides ad-hoc as well as scheduled reporting to share targeted reports on a daily, weekly or monthly basis. Subscribe to receive reports via email. Creates a custom snapshot of the report based on individual data permissions.
In-Memory Engine: Perform calculations and analyses at the speed of thought by querying through its super-fast parallel in-memory calculation engine, SPICE. Built from the ground up, it prepares data faster by replicating it in memory and updates in real-time as the underlying data changes.
Dashboards: Create data dashboards with 28 supported charts, such as filled maps, histograms, funnel charts, stacked area charts, waterfall charts and boxplots, as well as tables and pivot tables. View information based on business context by sorting on fields not shown in the visualization. Provide report readers with on-sheet controls to slice and dice data by filtering on fields. Offers multiple report layout customization options that include adding drop-downs with single-select or multi-select sorting.
ML-Based Insight: Get in-depth data insights through its out-of-the-box ML and NLP capabilities. Perform queries on data in seconds in simple language through its natural language attribute, Q. It learns from data patterns to provide accurate predictions based on historical trends. Detect anomalies and variations in data aggregates through scheduled anomaly detection.
Localization: Available as a global solution with support for 10 major languages: English, German, Spanish, French, Italian, Portuguese, Japanese, Korean, Simplified Chinese, and Traditional Chinese.

Sharing and Collaboration: KNIME Hub is an online repository for existing workflows, nodes and extensions that can be easily installed into a user’s workflow. Upload workflows and search for the components needed for projects.
In-database or Distributed Processing: Process data in-database or through a distributed cluster like Apache Spark for increasing scale. It has prebuilt workflows for in-database processing, like SQL Servers.
Model Predictions and Validation: Using machine learning and AI, produce predictive and prescriptive models. Use performance metrics such as AUC and R2 to verify models.
Visual Workflows: Using a drag-and-drop interface, compose a workflow with little to no coding. Prebuilt generic workflows and components can be downloaded from KNIME Hub.
Data Management: Handles all steps of the extract, transform and load processes. It can ingest, blend, prepare, cleanse and store structured and unstructured data. It can combine data types, including PDF, JSON, CSV and unstructured types like documents and images.
Data Visualizations: Compile analyses into reports with heat graphs, bar charts, scatter plots and more. Visualizations can be exported as PDFs, PowerPoints or other formats.
Tool Blending: Tools with unique domains can be combined within a workflow via native nodes. These include Python or R scripting, processing connectors, machine learning and AI.

Product Ranking

#80

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#89

among all
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Analyst Rating Summary

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User Sentiment Summary

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KNIME has a 'great' User Satisfaction Rating of 89% when considering 236 user reviews from 4 recognized software review sites.

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Synopsis of User Ratings and Reviews

Self-Service Analytics: QuickSight empowers business users to create and customize visualizations, dashboards, and reports without relying on IT or data scientists.

Cloud-Based Platform: QuickSight's cloud-based architecture eliminates the need for costly on-premise infrastructure and provides seamless access from anywhere with an internet connection.

Scalability and Performance: QuickSight handles large datasets efficiently, allowing users to analyze vast amounts of data in real-time.

Easy Integration: QuickSight seamlessly integrates with popular data sources, including Amazon Redshift, Amazon Aurora, and Salesforce, enabling users to access and analyze data from various systems.

Machine Learning Capabilities: QuickSight leverages machine learning algorithms to provide predictive analytics, anomaly detection, and automated insights, empowering users to uncover hidden patterns and make data-driven decisions.

Collaboration and Sharing: QuickSight facilitates collaboration among teams by allowing users to share dashboards, reports, and insights securely.

Functionality: It provides a comprehensive set of nodes and functions to process large quantities of data, as noted by 69% of users who referred to functionality.

User Friendly: It is intuitive and easy to use, as noted by 79% of reviewers who refer to ease of use.

Connectivity: Around 77% of users who talked about connectivity mentioned its ability to seamlessly connect and integrate with multiple sources.

Cost: All users were happy that the solution is available free of charge, with no data limits.

Pricing Structure: Complicated pricing model that can lead to unexpected costs for advanced features and usage.

Limited Customization: Pre-built dashboards and visualizations may not fully meet specific business requirements, limiting customization options.

Data Connectivity: May not seamlessly integrate with all data sources, requiring additional effort for data preparation and ingestion.

Limited Advanced Analytics: Lacks certain advanced analytical capabilities, such as predictive modeling and machine learning algorithms, which may be essential for deeper insights.

User Interface: Some users find the user interface to be cluttered and not as intuitive as other BI tools, impacting the ease of use.

Performance: Nearly 95% of reviewers who mentioned performance said that the solution runs slowly and uses too much CPU and memory.

Visualization: Approximately 67% of users who specified visualization talked about its lack of proper visualization options.

Support: About 67% of users who reviewed support mentioned how hard it is to get proper documentation or support.

Learning Curve: KNIME has a steep learning curve, according to about 64% of users who mentioned the learning curve.

Amazon QuickSight has received positive user reviews for its ease of use, intuitive interface, and quick data visualization capabilities. Users find it simple to connect to data sources, create dashboards, and generate insights. The drag-and-drop functionality and pre-built templates make it accessible to users of all technical skill levels. QuickSight's strengths include its integration with other Amazon Web Services (AWS) products, such as Amazon Redshift and Amazon S3, allowing for seamless data analysis. It also offers advanced features like machine learning-powered insights and natural language querying, providing users with deeper analytical capabilities. Compared to similar products, QuickSight is praised for its cost-effectiveness and scalability. Users appreciate its pay-as-you-go pricing model and the ability to handle large datasets without performance issues. However, some users have expressed concerns about the limited customization options and the lack of certain advanced features found in competing products. Overall, Amazon QuickSight is well-suited for businesses and organizations looking for an easy-to-use and affordable business intelligence solution. Its intuitive interface, integration with AWS products, and scalability make it a valuable tool for data visualization, analysis, and decision-making.

KNIME is a robust open-source solution with cross-platform interoperability. It integrates with a range of software, such as JS, R, Python and Spark. With a variety of nodes and functions, it can process large datasets with a decent level of control in each step. Workflows are displayed as connected nodes, making it easy to isolate and fix specific steps. It also contains built-in tools to create and test supervised and unsupervised machine learning models. Users found the UI very intuitive and flexible. On the flip side, they found the tool visually lacking and primitive. The system also has performance and stability issues. Processing big data is very time consuming since the platform isn’t cloud-based. Users reported excessive memory usage as well. It also lacks reporting or monitoring features. Decent technical knowledge is required to fully leverage its capabilities.

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