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KNIME
by KNIME | Founded 2008, Zurich, Switzerland
Categories:
- Big Data Analytics Tools
- Big Data Visualization Tools
- Business Intelligence Tools
- Cloud BI Solutions
- Data Analytics Software
- Data Cleaning Tools
- Data Discovery Tools
- Data Management Tools
- Data Preparation Software
- Data Warehouse Tools
- Diagram Software
- Ecommerce Analytics Software Tools
- HR Analytics Software
- OLAP Tools
- Predictive Analytics Software
- Qualitative Data Analysis Software
- Self-Service BI Tools
What is KNIME?
Industry Specialties: Serves all industries
KNIME is an open-source end-to-end data analytics solution. It utilizes visual workflows with drag-and-drop functionality and thousands of nodes to lessen the data analytics learning curve data, with more than 1,800 prebuilt default workflows for streamlined setup.
It allows for data ingestion, preparing, cleansing, analyzing and visualizing. It can be scaled for deeper analytics through integrations with sophisticated data modeling capabilities. It can be hosted on-premise or in the cloud through Microsoft Azure.
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KNIME Pricing
Based on our most recent analysis, KNIME pricing and cost details are described here:
- Price
- $$$$$
- Starting From
- Still gathering data
- Pricing Model
- Still gathering data
- Free Trial
- Still gathering data
KNIME Benefits and Insights
Why use KNIME?
Key differentiators & advantages of KNIME
- Open-Source: Join a network of thousands of users, enabling collaboration and support. The source code is free to download and access.
- Free To Use: Save money by getting access to all of the platform’s features for free. Licensed productivity and collaboration extensions are available at a cost.
- Increased Business Intelligence: Get digestible, actionable data to make informed business decisions. Aggregating large datasets into predictive and prescriptive models via comprehensive visualizations and summary statistics gives users projections for the best course of action.
- Scalable: Obtain access to big data by scaling up the project in-platform. Integrations to distributed and multi-threaded data processing allow projects to grow.
- End-To-End Analytics: It is capable of handling some tasks from start to finish without integrations. Additional integrations may be required for increasing scale and completing more sophisticated analytics.
Industry Expertise
The KNIME platform originated in 2004 at the University of Konstanz in Germany. It has more than 15,000 regular users in at least 60 countries, spanning industries such as pharmaceutical, CRM, financial analysis and life sciences.
KNIME Reviews
Average customer reviews & user sentiment summary for KNIME:
Synopsis of User Ratings and Reviews
Based on an aggregate of KNIME reviews taken from the sources above, the following pros & cons have been curated by a SelectHub Market Analyst.
Pros
Cons
Researcher's Summary:
KNIME is a robust open-source solution with cross-platform interoperability. It integrates with a range of software, such as JS, R, Python and Spark. With a variety of nodes and functions, it can process large datasets with a decent level of control in each step. Workflows are displayed as connected nodes, making it easy to isolate and fix specific steps. It also contains built-in tools to create and test supervised and unsupervised machine learning models. Users found the UI very intuitive and flexible. On the flip side, they found the tool visually lacking and primitive. The system also has performance and stability issues. Processing big data is very time consuming since the platform isn’t cloud-based. Users reported excessive memory usage as well. It also lacks reporting or monitoring features. Decent technical knowledge is required to fully leverage its capabilities.
Key Features
- Sharing and Collaboration: KNIME Hub is an online repository for existing workflows, nodes and extensions that can be easily installed into a user’s workflow. Upload workflows and search for the components needed for projects.
- In-database or Distributed Processing: Process data in-database or through a distributed cluster like Apache Spark for increasing scale. It has prebuilt workflows for in-database processing, like SQL Servers.
- Model Predictions and Validation: Using machine learning and AI, produce predictive and prescriptive models. Use performance metrics such as AUC and R2 to verify models.
- Visual Workflows: Using a drag-and-drop interface, compose a workflow with little to no coding. Prebuilt generic workflows and components can be downloaded from KNIME Hub.
- Data Management: Handles all steps of the extract, transform and load processes. It can ingest, blend, prepare, cleanse and store structured and unstructured data. It can combine data types, including PDF, JSON, CSV and unstructured types like documents and images.
- Data Visualizations: Compile analyses into reports with heat graphs, bar charts, scatter plots and more. Visualizations can be exported as PDFs, PowerPoints or other formats.
- Tool Blending: Tools with unique domains can be combined within a workflow via native nodes. These include Python or R scripting, processing connectors, machine learning and AI.
Limitations
Some of the product limitations include:
- It does not offer monthly billing, only annual
- It requires five users and eight cores to qualify for an annual subscription
- Lacks integration with Jupyter Notebooks
- Doesn’t offer phone support
- Error messages may cause interruptions.
- Lacks customization for default nodes
Suite Support
Because of the open-source nature of KNIME, it relies heavily on a community forum for support. The forum has nearly 10,000 users, including KNIME personnel, to assist users. Traditional support is described below.
mail_outlineEmail:Email support is fulfilled by the ticketing system described below.
phonePhone: Phone support is not provided.
schoolTraining: The vendor offers a “Learning” section on its website, offering FAQs, documentation, white papers, webinars and training courses. Courses take place over a period of time and vary from free or for a price. Certification is available.
local_offerTickets: Users can submit a support ticket through the “Contact” page on the vendor’s website.