KNIME Analytics Platform is an on-premise data analytics platform, which helps small to large businesses create and manage data science applications or services. The drag-and-drop interface allows users to create workflow prototypes, monitor performance and streamline in-data processing or distributed computing using Apache Spark framework.
Key features of KNIME Analytics Platform include predictions, data cleaning, filtering, data model validation and reporting. Users can build machine learning models to automate data processes such as classification, dimensionality reduction, regression and cluster analysis, leveraging the use of artificial intelligence across operations.
Businesses can use KNIME Analytics Platform to integrate data from any source in unstructured data types or multiple formats. It also allows users to load and transfer data from warehouses including Oracle, Microsoft SQL, Avro, Parquet and more. The solution can be hosted on a cloud environment using Microsoft Azure and AWS. Pricing is available on request and support is extended via online measures.
Yashoda W. Secteur: Développement de programmes Nombre d'employés: Auto-entrepreneur
This is a good software which helps to solve statistical ptoblems,mathematical problems and also algorithm problems.so in Engineering there have lots of problems belongs to aforesaid kind of problems.so this can be useful for pre- Engineers.
This software gives most accurate answers and it has more sensitivity & most of values have precision.
sometimes error messages displaying while works are in progress
Meliksah T. Secteur: Électronique grand public Nombre d'employés: 5 001-10 000 employés
KNIME is a very good Data Analytics software overall. If the team can handle the slowness issue, it's great for both computer science associates and other employees.
There are multiple features that is great about KNIME.
- Since it's a visual UI that you work on, it's possible to track down what's going on, similar to an ETL tool. That's something that does not exist on programming.
- Community continuously develops Nodes so its like an organism that grows.
Its upsides come with downsides:
- Since it's high level (so to speak in CS manner) software. It is far from computer language, and that makes it very slow. It gets even slower more nodes and extensions are installed which was again, an upside.
- Nodes have customization and parameter management but they are not as customizable as Python/R libraries, though for that Python/R node can be used.
Ivan C. Secteur: Produits pharmaceutiques Nombre d'employés: 10 001+ employés
It is a good tool for small business owners, but it lacks the scalability for larger audiences.
It has a well built GUI for visualizing the pipeline for your data-driven applications, and it also comes with a KNIME Server for CRAN job application and deployment of your software
The UI is a little laggy and files can get excessively large, run time and speed is also slow when integrating with other scripting languages.
Ferhat D. Secteur: Services et technologies de l'information Nombre d'employés: 5 001-10 000 employés
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.
- 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.
- 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
Stephen S. Secteur: Gestion d'organisme à but non lucratif Nombre d'employés: 201-500 employés
We were able to build a reproducible workflow for analyzing our data and creating actionable insights.
KNIME desktop is a powerful tool for building analytical workflows. The visual interface is extremely helpful. They also have extensions to integrate other tools like R and Python into the workflows. Best of all you can share your workflows with others - great for reproducible research. There are built in tools for many types of supervised and unsupervised machine learning. The desktop application is free and open source. The support community on the KNIME website is very active and responsive. To extend the features you can purchase KNIME server.
Like any new tool there is a learning curve. However, they have lots of videos, examples and an active support community. There are some features that are not intuitive, such as how to use flow variables. In general I have found that I use R much less now and do most of my analysis in KNIME. KNIME is primary drag and drop and requires little to no coding.