--- description: ¿Qué piensan los usuarios de KNIME Analytics Platform? Lee las reseñas y opiniones verificadas, descubre sus características y el nivel de usabilidad, consulta los precios y conoce las ventajas y desventajas de KNIME Analytics Platform gracias a Capterra Chile. image: https://gdm-localsites-assets-gfprod.imgix.net/images/capterra/og_logo-e5a8c001ed0bd1bb922639230fcea71a.png?auto=format%2Cenhance%2Ccompress title: KNIME Analytics Platform - Opiniones, precios y características - Capterra Chile 2026 --- Breadcrumb: [Inicio](/) > [Software de análisis predictivo](/directory/30945/predictive-analytics/software) > [KNIME Analytics Platform](/software/158739/knime-analytics-platform) # KNIME Analytics Platform Canonical: https://www.capterra.cl/software/158739/knime-analytics-platform Página: 1 de 2\ Siguiente: [Página siguiente](https://www.capterra.cl/software/158739/knime-analytics-platform?page=2) > Funciones matemáticas y estadísticas, control de flujo de trabajo, algoritmos predictivos avanzados y de aprendizaje automático y más funciones para los científicos de datos. > > Veredicto: 26 usuarios lo han valorado con **4.6/5**. De los mejores valorados en **Probabilidad de recomendación**. ----- ## Estadísticas y valoraciones rápidas | Métrica | Calificación | Detalles | | **En general** | **4.6/5** | 26 Opiniones | | Facilidad de uso | 4.5/5 | Basado en las opiniones generales | | Asistencia al cliente | 4.0/5 | Basado en las opiniones generales | | Relación calidad-precio | 4.7/5 | Basado en las opiniones generales | | Funciones | 4.5/5 | Basado en las opiniones generales | | Porcentaje de recomendaciones | 90% | (9/10 Probabilidad de recomendación) | ## Sobre el proveedor - **Empresa**: KNIME.COM - **Ubicación**: Zurich, Suiza ## Contexto comercial - **Precio inicial**: EUR 0,00 - **Modelo de precios**: (versión gratuita disponible) - **Público objetivo**: Trabajador autónomo, 2-10, 11-50, 51-200, 201-500, 501-1.000, 1.001-5.000, 5.001-10.000, 10.000+ - **Despliegue y plataformas**: Cloud, SaaS, Web, Mac (desktop), Windows (desktop), Linux (desktop), Linux (local) - **Idiomas admitidos**: inglés - **Países disponibles**: Afganistán, Albania, Alemania, Andorra, Angola, Anguila, Antigua y Barbuda, Arabia Saudita, Argelia, Argentina, Armenia, Aruba, Australia, Austria, Azerbaiyán, Bahamas, Bangladés, Barbados, Baréin, Belice y 208 más ## Funciones - API - Análisis de Big Data - Análisis predictivo - Análisis visual - Aprendizaje profundo - Arrastrar y soltar - Biblioteca de algoritmos ML - Cartografiado de datos - Conectores de datos - Creación de informes ad hoc - Creación de informes y estadísticas - Creación de informes/análisis - Data Clustering - Descubrimiento de datos - Extracción de datos - Formación de modelos - Gestión de flujos de trabajo - Herramientas de colaboración - IA y aprendizaje automático - Importación y exportación de datos - Modelado predictivo - Modelado y simulación - Métricas de rendimiento - Panel de comunicaciones - Previsión - Procesamiento de lenguaje natural - Publishing/Sharing - Time Series Analysis - Varias fuentes de datos - Visualización de datos ## Integraciones (en total: 26) - Amazon Aurora - Amazon Comprehend - Amazon DynamoDB - Amazon EMR - Amazon S3 - Apache Hive - Azure Blob Storage - Azure Data Lake Storage - Azure Databricks - Azure Synapse Analytics - ChatGPT - Cloudera Enterprise - Databricks - Google Cloud Storage - Google Sheets … y 11 integraciones más ## Opciones de asistencia - E-mail/Help Desk - Preguntas frecuentes/foro - Base de conocimientos - Asistencia telefónica ## Categoría - [Software de análisis predictivo](https://www.capterra.cl/directory/30945/predictive-analytics/software) ## Categorías relacionadas - [Software de análisis predictivo](https://www.capterra.cl/directory/30945/predictive-analytics/software) - [Herramientas de Machine Learning](https://www.capterra.cl/directory/31103/machine-learning/software) - [Herramientas BI](https://www.capterra.cl/directory/23/business-intelligence/software) - [Software de preparación de datos](https://www.capterra.cl/directory/32747/data-preparation/software) - [Programas de análisis estadístico](https://www.capterra.cl/directory/30752/statistical-analysis/software) ## Alternativas 1. [Minitab](https://www.capterra.cl/software/109731/minitab-statistical-software) — 4.5/5 (391 reviews) 2. [Tableau](https://www.capterra.cl/software/77260/tableau) — 4.6/5 (2362 reviews) 3. [Domo](https://www.capterra.cl/software/119119/domo) — 4.3/5 (331 reviews) 4. [JMP](https://www.capterra.cl/software/151815/jmp-statistical-software) — 4.5/5 (53 reviews) 5. [Max AI](https://www.capterra.cl/software/152303/answerrocket) — 4.6/5 (15 reviews) ## Opiniones ### "Well created open source for data analysis\!" — 5.0/5 > **Rochelle** | *28 de enero de 2023* | Tecnología y servicios de la información | Valoración de la recomendación: 10.0/10 > > **Puntos a favor**: One of the pros is of course doesn't require license fee. It is also an open source that can connect to Python and R that is capable of customization. Need to mention also the good community support. > > **Puntos en contra**: It took time to understand the functionalities and familiarize the user interface. ----- ### "Data Science 101 Platform for non-IT people" — 4.0/5 > **Ferhat** | *25 de enero de 2020* | Tecnología y servicios de la información | Valoración de la recomendación: 7.0/10 > > **Puntos a favor**: - Its ease of use makes it possible for non-IT, non-developer, non-CS background people to make data manipulation, preprocessing, mining, visualization and modelling. - It has a graphical interface with nodes and connections so that you don't need to know Python/R to make predictive models or association rules/recommendation systems. - There's a vast library of functions - Even more functions are created by the community so non-existing customized functions are created by the community, via existing functions. - The visual flow of data makes it easy to understand and interpret it. - It teaches the CRISP-DM methodology in an intuitive way thanks to its graphical user interface - It can connect to SQL and similar servers so that the data can be read directly. - It is possible to write own Python/R script for custom needs. > > **Puntos en contra**: - Custom needs are hard to carry out. - Functions have limited abilities and parameters - Data visualization is weak and relatively primitive - Model development is easy but deployment is hard - It is very slow unfortunately and I think this is KNIME's most important drawback > > It was the tool I learned the Data Science in the first place. So it is really good and intuitive with its graphical interface. For example you understand train-test split very well because you literally see the split as you work on it. As I progressed and needed more functions and more custom solutions, I started using Python scripts and solved it like that. So it gave me all these abilities. ----- ### "Powerful Visual Platform for Data Analysis and Workflow Automation" — 5.0/5 > **Usuario verificado** | *17 de septiembre de 2026* | Administración educativa | Valoración de la recomendación: 10.0/10 > > **Puntos a favor**: I like the visual workflow approach because it makes data preparation and analysis easier to understand without writing everything from scratch. The range of nodes for data cleaning, transformation, analysis, and machine learning is useful, and I also like being able to connect different data sources in the same workflow. Once a workflow is set up, it is easy to reuse and modify for similar tasks. > > **Puntos en contra**: The platform can feel a little overwhelming at first because there are so many nodes and configuration options. Some workflows also require additional setup before everything works smoothly, especially when connecting external tools or larger datasets. Documentation is generally useful, but beginners may still need some time to get comfortable with the platform. > > Overall, my experience with KNIME Analytics Platform has been positive. I find it useful for data cleaning, transformation, exploratory analysis, and building repeatable analytics workflows. The visual interface makes complex data processes easier to follow, while the ability to combine different tools and data sources adds flexibility. The main learning curve is getting familiar with the large number of available nodes and understanding how to structure workflows efficiently. ----- ### "Solid Platform for Small Datasets and Broad Data Connectivity" — 4.0/5 > **Usuario verificado** | *1 de mayo de 2020* | Salud, bienestar y deporte | Valoración de la recomendación: 6.0/10 > > **Puntos a favor**: There is a wide range of tools to process and prep data in the platform natively and additional tools that can be download within the platform. The ability to customize the settings for most of the tools allows the user to adjust the output. Even more technical settings, like hyperparameter tuning, can be done in the tool UI. There are numerous input and output options and types. > > **Puntos en contra**: Pulling in very basic files, like Excel spreadsheets can be a bit challenging where other platforms handle files with ease. Also, database connections are not seamless. The Java memory errors also limit the size of data that can be processed without making manual adjustments to settings. Lastly, not being a cloud-based platform, processing big data is very time-consuming. > > The two main reasons we used KNIME were to process and prep data, then to conduct machine learning by training models and processing predictions. KNIME is great with data prep and blend as long as the data set is small to medium in size (\< 4GB). There were areas where we struggled and that was when models were more complex (\> 50 variables) and being able to deploy and schedule jobs. We had to download JDBC drivers for our database connections, which was not something we had to do with other platforms. ----- ### "Great for all types of data scientists" — 5.0/5 > **Usuario verificado** | *26 de septiembre de 2020* | Educación superior | Valoración de la recomendación: 9.0/10 > > **Puntos a favor**: Some drag and drop tools for machine learning are really limited, but KNIME is not. There are a ton of capabilities of the tool that are built in, and there are even more that are available online, like AutoML. It gives citizen data scientists the ability to create good models without knowing a programming language, and it increases the bandwidth of actual data scientists by allowing them to easily create more models and experiments. > > **Puntos en contra**: Of course, it is more limited than a programming language, and if you're familiar with building models programmatically, there is a learning curve that will slow you down and limit you at first. > > I have had a very positive experience with KNIME and like it a lot more than other drag and drop machine learning tools I have tried out. ----- Página: 1 de 2\ Siguiente: [Página siguiente](https://www.capterra.cl/software/158739/knime-analytics-platform?page=2) ## Enlaces - [Ver en Capterra](https://www.capterra.cl/software/158739/knime-analytics-platform) ## Esta página se encuentra disponible en los siguientes idiomas | Local | URL | | de | | | de-AT | | | de-CH | | | en | | | en-AE | | | en-AU | | | en-CA | | | en-GB | | | en-IE | | | en-IL | | | en-IN | | | en-NZ | | | en-SG | | | en-ZA | | | es | | | es-AR | | | es-CL | | | es-CO | | | es-CR | | | es-DO | | | es-EC | | | es-MX | | | es-PA | | | es-PE | | | fr | | | fr-BE | | | fr-CA | | | fr-LU | | | ja | | ----- ## Datos estructurados