--- description: KNIME Analytics Platform : avantages et inconvénients. Lisez les avis clients, consultez les prix et découvrez les fonctionnalités. image: https://gdm-localsites-assets-gfprod.imgix.net/images/capterra/og_logo-e5a8c001ed0bd1bb922639230fcea71a.png?auto=format%2Cenhance%2Ccompress title: KNIME Analytics Platform - Avis, notes, prix et abonnements - Capterra Canada 2026 --- Breadcrumb: [Accueil](/) > [Logiciels d'analyse prédictive](/directory/30945/predictive-analytics/software) > [KNIME Analytics Platform](/software/158739/knime-analytics-platform) # KNIME Analytics Platform Canonical: https://fr.capterra.ca/software/158739/knime-analytics-platform Page : 1/2\ Suivant: [Page suivante](https://fr.capterra.ca/software/158739/knime-analytics-platform?page=2) > Fonctions mathématiques et statistiques, contrôle du flux de travail, algorithmes de machine learning et prédictifs avancés et bien plus pour les scientifiques de données. > > Conclusion : 26 utilisateurs lui ont donné la note de **4.6/5**. Figure au meilleur classement pour **Probabilité de recommander le produit**. ----- ## Statistiques et notes | Métrique | Notation | En détail | | **Note globale** | **4.6/5** | 26 Avis | | Simplicité | 4.5/5 | D'après l'ensemble des avis | | Support client | 4.0/5 | D'après l'ensemble des avis | | Rapport qualité-prix | 4.7/5 | D'après l'ensemble des avis | | Fonctionnalités | 4.5/5 | D'après l'ensemble des avis | | Pourcentage de recommandation | 90% | (9/10 Probabilité de recommander le produit) | ## À propos du vendeur - **Entreprise**: KNIME.COM - **Pays**: Zurich, Suisse ## Contexte commercial - **À partir de**: 0,00 € - **Type de licence**: (Version gratuite disponible) - **Public cible**: Travailleur autonome, 2–10, 11–50, 51–200, 201–500, 501–1 000, 1 001–5 000, 5 001–10 000, 10 000+ - **Déploiement et plateformes**: Cloud, SaaS, web, Mac (ordinateur), Windows (ordinateur), Linux (ordinateur), Linux (sur site) - **Langues**: anglais - **Pays disponibles**: Afghanistan, Afrique du Sud, Albanie, Algérie, Allemagne, Andorre, Angola, Anguilla, Antigua-et-Barbuda, Arabie saoudite, Argentine, Arménie, Aruba, Australie, Autriche, Azerbaïdjan, Bahamas, Bahreïn, Bangladesh, Barbade et 208 de plus ## Fonctionnalités - API - Analyse prédictive - Analyse visuelle - Analytique Big Data - Bibliothèque d'algorithmes de ML - Connecteurs de données - Data Clustering - Data discovery - Deep learning - Extraction de données - Fonction de glisser-déposer - Gestion des flux de travail - Importation et exportation de données - Intelligence artificielle et apprentissage automatique - Mappage de données - Mesure des performances - Modèle de formation - Modélisation et simulation - Modélisation prédictive - Outils de collaboration - Prévision - Publishing/Sharing - Rapports ad hoc - Rapports et analyses - Rapports et statistiques - Sources de données multiples - Tableau de bord - Time Series Analysis - Traitement automatique du langage naturel - Visualisation de données ## Intégrations (26 au total) - 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 ... et 11 intégrations supplémentaires ## Ressources d'aide - Service client/courriel - FAQ/forums - Base de connaissances - Support téléphonique ## Category - [Logiciels d'analyse prédictive](https://fr.capterra.ca/directory/30945/predictive-analytics/software) ## Catégories connexes - [Logiciels d'analyse prédictive](https://fr.capterra.ca/directory/30945/predictive-analytics/software) - [Outils d'apprentissage automatique](https://fr.capterra.ca/directory/31103/machine-learning/software) - [Outils BI (Business intelligence)](https://fr.capterra.ca/directory/23/business-intelligence/software) - [Logiciels de préparation des données](https://fr.capterra.ca/directory/32747/data-preparation/software) - [Logiciels de statistiques](https://fr.capterra.ca/directory/30752/statistical-analysis/software) ## Logiciels similaires 1. [Zoho Analytics](https://fr.capterra.ca/software/129749/zoho-analytics) — 4.4/5 (361 reviews) 2. [SAS Viya](https://fr.capterra.ca/software/1043140/sas-viya) — 4.4/5 (12 reviews) 3. [Tableau](https://fr.capterra.ca/software/77260/tableau) — 4.6/5 (2362 reviews) 4. [Domo](https://fr.capterra.ca/software/119119/domo) — 4.3/5 (331 reviews) 5. [Stata](https://fr.capterra.ca/software/119880/stata) — 4.5/5 (183 reviews) ## Avis ### "Well created open source for data analysis\!" — 5.0/5 > **Rochelle** | *28 janvier 2023* | Services et technologies de l'information | Taux de recommandation : 10.0/10 > > **Avantages**: 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. > > **Inconvénients**: 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 janvier 2020* | Services et technologies de l'information | Taux de recommandation : 7.0/10 > > **Avantages**: - 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. > > **Inconvénients**: - 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 > **Utilisateur vérifié** | *17 septembre 2026* | Gestion de l'enseignement | Taux de recommandation : 10.0/10 > > **Avantages**: 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. > > **Inconvénients**: 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 > **Utilisateur vérifié** | *1 mai 2020* | Santé, bien-être et fitness | Taux de recommandation : 6.0/10 > > **Avantages**: 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. > > **Inconvénients**: 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 > **Utilisateur vérifié** | *26 septembre 2020* | Enseignement supérieur | Taux de recommandation : 9.0/10 > > **Avantages**: 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. > > **Inconvénients**: 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. ----- Page : 1/2\ Suivant: [Page suivante](https://fr.capterra.ca/software/158739/knime-analytics-platform?page=2) ## Liens - [Afficher sur Capterra](https://fr.capterra.ca/software/158739/knime-analytics-platform) ## Cette page est disponible dans les langues suivantes. | Langue | 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 | | ----- ## Données structurées