KNIME and IBM Watson Studio compete in the data analytics tools category. While KNIME provides accessibility with its no-code platform, IBM Watson Studio stands out with its advanced AI and machine learning capabilities.
Features: KNIME offers easy integration with languages such as R, Python, and Java. It is an open-source platform with a strong ETL capability, making data preparation straightforward for beginners. IBM Watson Studio is known for seamless data integration and powerful AI features, supporting big data analytics and complex models suited for enterprise environments.
Room for Improvement: KNIME users suggest enhancements in data visualization and efficient handling of very large datasets. Improved documentation and more database integrations are also desired. Suggestions for IBM Watson Studio include a more streamlined interface and clearer deployment guidelines to enhance usability.
Ease of Deployment and Customer Service: KNIME is ideal for on-premises deployment, benefiting from active community support but lacking in immediate technical assistance. IBM Watson Studio excels in cloud deployments, offering reliable support, crucial for business-critical applications.
Pricing and ROI: KNIME is cost-effective as an open-source platform, favored for its free desktop version. IBM Watson Studio, while more expensive, offers extensive capabilities that justify its cost in enterprise settings, with pricing considered reasonable for the features provided.
The product offers a significant return on investment through its scalability and integration capabilities.
My customers have seen returns on investment through increased efficiency, automated calculations, improved accuracy in pricing, and reduced staffing needs due to the automation.
The support quality depends on the SLA or the contract terms.
The community access is weak, which limits the ability to engage in discussions and find documentation and examples of similar cases effectively.
While they cannot always provide immediate answers, they are generally efficient and simplify tasks, especially in the initial phase of learning KNIME.
Watson Studio is very scalable.
I rate IBM Watson Studio seven out of ten for scalability because while it scales, it requires significant resources to do so, making it expensive compared to some competitors.
Expertise in optimization is necessary to manage such issues effectively.
IBM should work on optimizing the user interface and enhancing the product's accessibility for medium and small enterprises.
One area that could be improved is the backup and restoration of the database and the overall database configuration.
For graphics, the interface is a little confusing.
The machine learning and profileration aspects are fascinating and align with my academic background in statistics.
IBM Watson Studio is considered rather expensive, with a rating of six or seven.
This capability saves a significant amount of time by automating processes that typically involve manual work, such as data cleaning, feature engineering, and predictive analytics.
It integrates well with other platforms and offers good scalability.
KNIME is simple and allows for fast project development due to its reusability.
KNIME is more intuitive and easier to use, which is the principal advantage.
IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.
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.”
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