Is Knime difficult?
KNIME Analytics is a very complex tool, so it has a steep learning curve.
Is it easy to learn Knime?
KNIME Analytics Platform is the free, open-source software for creating data science. Our enterprise-grade, open source platform is fast to deploy, easy to scale, and intuitive to learn. Below are some resources which may help you to start using KNIME.
Is Knime any good?
KNIME Analytics Platform Reviews. “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.” “Overall KNIME is a solid ETL tool which can automate most of the daily workflows.”
Is Knime widely used?
As of 2012, KNIME is in use by over 15,000 actual users (i.e. not counting downloads but users regularly retrieving updates when they become available) not only in the life sciences and also at banks, publishers, car manufacturer, telcos, consulting firms, and various other industries as well as at a large number of …
Is Knime good for machine learning?
KNIME Analytics Platform is the strongest and most comprehensive free platform for drag-and-drop analytics, machine learning, statistics, and ETL that I’ve found to date. The fact that there’s neither a paywall nor locked features means the barrier to entry is nonexistent.
Is Knime better than Python?
Python, with its extensive library and support (Books and Online Support community), is a great tool for anyone with a programming background. Knime is a great tool of choice with people with no programming background and looking for a free tool.
What is orange tool?
Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative rapid qualitative data analysis and interactive data visualization.
Is Knime really free?
KNIME Analytics Platform is 100% free. Higher tiers of KNIME Server allow for use of the REST API and WebPortal. Those features allow you to automate workflow deployment, execute workflows remotely from another service, and create an interactive hub for users.
How good is alteryx?
Critical Review Alteryx is a great data prep platform, but abstracts away too much of the data science process. The Server experience is quite poor and does not allow precise configuration or control. Great tool for an analyst used to working in Excel, but doesn’t cut it on production grade efforts.
Which company uses KNIME?
Companies Currently Using KNIME Analytics Platform
| Company Name | Website | Top Level Industry |
|---|---|---|
| Fiserv | fiserv.com | Technical |
| Eaton Corporation | eaton.com | Business Services |
| AMERIPRISE FINANCIAL | ameriprise.com | Finance |
| Betsson Group | betssongroup.com | Hospitality |
Why is KNIME popular?
How is working with KNIME a good experience?
Working with KNIME has been a very productive and pleasant experience. They seem to know the analytics filed (needs and wants of the customers) and their software offerings to prescribe an excellent match for powerful analytics solution. The analytics platforms itself is very …
What are the capabilities of KNIME analytics platform?
The KNIME workflows that we built have many different capabilities, ranging from data extraction, pre-processing, model training and optimization. We also build some self-services analytics platform using KNIME as well as automation tools. Easy to use without much knowledge of coding. Connection to other languages such as JS, R, Python, etc.
Is it hard to get customer service from KNIME?
KNIME’s HQ is in Europe, which makes it hard for US companies to get customer service in time and on time. Their customer service also takes on average 1 to 2 weeks to follow up with your request. KNIME’s documentation is also helpful but it does not provide you all the answers you need some of the time.
How is KNIME used in the data pipeline?
KNIME is used as a bridge piece of software that connects multiple, disparate data sources into a single data pipeline for further analysis downstream. Some level of transformation is done in the processing, mainly for data cleansing, but most of that is left to custom code further on in the pipeline.