KNIME and Domino Data Science Platform are products competing in the data science and analytics space. Domino often has an advantage due to its comprehensive features, but KNIME stands out for cost-effectiveness.
Features: KNIME is known for its open-source platform, flexible data processing, and extensibility through diverse integrations and connectors. Domino offers collaborative model development, deployment, and provides advanced tools for version control and project management, resulting in a feature-rich environment.
Ease of Deployment and Customer Service: KNIME has a straightforward deployment process suitable for small to mid-sized teams with self-service support resources. Domino's sophisticated deployment meets enterprise needs and integrates seamlessly into existing infrastructures, with customer service as a key differentiator.
Pricing and ROI: KNIME offers a low entry barrier with minimal setup costs and maintains a favorable ROI due to its open-source model. Domino requires a higher initial investment, justified by its advanced features and scalability, delivering substantial long-term ROI in larger environments, highlighting KNIME's affordability against Domino's value-driven approach.
Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.
KNIME is an open-source analytics software used for creating data science that is built on a GUI based workflow, eliminating the need to know code. The solution has an inherent modular workflow approach that documents and stores the analysis process in the same order it was conceived and implemented, while ensuring that intermediate results are always available.
KNIME supports Windows, Linux, and Mac operating systems and is suitable for enterprises of all different sizes. With KNIME, you can perform functions ranging from basic I/O to data manipulations, transformations and data mining. It consolidates all the functions of the entire process into a single workflow. The solution covers all main data wrangling and machine learning techniques, and is based on visual programming.
KNIME Features
KNIME has many valuable key features. Some of the most useful ones include:
KNIME Benefits
There are many benefits to implementing KNIME. Some of the biggest advantages the solution offers include:
Reviews from Real Users
Below are some reviews and helpful feedback written by PeerSpot users currently using the KNIME solution.
An Emeritus Professor at a university says, “It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional. It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.”
Benedikt S., CEO at SMH - Schwaiger Management Holding GmbH, explains, “All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function. Technical support has been extremely responsive so far. The solution has a very strong and supportive community that shares information and helps each other troubleshoot. The solution is very stable. The initial setup is pretty simple and straightforward.”
Piotr Ś., Test Engineer at ProData Consult, says, “What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.”
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
