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KNIME Analytics Platform is an open-source platform that allows or helps the users to automate the data science process to create a data science application or services. With the use of the KNIME Analytics Platform, it possible to create a visual workflow with a drag and drop graphical user interface set up which does not necessarily require or need for coding. The use of the nodes and establishing the interrelation helps in creating a workflow that can be executed locally or on the KNIME web portal after the workflow is deployed onto the KNIME server. Through the use of KNINE, it possible to create a guided analytics process as a workflow that can help in automating the process.
It is possible to perform functions ranging from basic I/O functions to functions related to data manipulations, transformations and data mining into a single workflow using KNIME.
The KNIME Analytics Platform is one of the leading open-source solutions available for data-driven innovation, that is designed to help in discovering the potential hidden meaning in the data. The Analytics Platform provides the option of data mining to get new fresh insights and help in predicting possible new futures. There is a robust range of commercial extensions present in the platform that helps organisations to take their collaboration, productivity and performance to the next level.
Over a decade, the KNIME platform has been working with a thriving community or group of data scientists from 60 or more countries to help in managing all kinds of possible data. This data can be numbered, images complex network and being able to apply simple statistics to big data analytics. A vibrant data scientist group in over 60 countries has been working with our platform on all kinds of data for over a decade: from numbers to pictures, molecules to people, signals to complex networks, and simple statistics to big data analysis.
The software platform available from KNIME is an enterprise-grade software platform and two complementary tools. These complementary tools or platforms are open-source KNIME Analytics Platform which is developed for creating data science and a commercial KNIME Server for production using data science.
KNIME Analytics Platform
KNIME Analytics Platform is an open-source software platform created to help in data science development. The platform is intuitive, transparent and constantly integrating with new technology or development to help in expanding the field of data science. With this KNIME platform, the user has the ability to perform data comprehension, data science and data analytics application design.
KNIME Server is a business or enterprise software that is used for team collaboration, automation, management and deployment of data science workflows in terms of analytical application and services. Via the use of KNIME WebPortal or the use of REST APIs, non-experts are given or have access to data science workflow.
Ways KNIME can help
- Gather & Wrangle
In this option of KNIME, it possible getting access to all your data sources. KNIME Software helps in connecting to varied data sources un one intuitive visual workflow environment. There is a broad range of additional functionalities available in the workflow environment that helps in the transformation and cleaning of the data.
Features of KNIME:
- With the option of Gather & Wrangle in KNIME, it is possible to spend time where it matters as it is possible to combine the multiple dataset from the multiple sources and transform them into the required shape or structure to generate useful insights quickly.
- With the KNIME Analytics Platform, it is possible to gain access or connections to data storage applications like database and data warehouses or from cloud and external services, which provides a variety of file formats. This concept and functionality provide the user to do their job well and on their own terms without requirement or dependencies on the central IT structure.
- The workflows created with the help of the KNIME Analytics Platform helps in delivering consistent and reliable results to each team member as there is automatic documentation of each step at the time of data wrangling process.
- Model & Visualisation
Before even getting to the process of machine learning and artificial intelligence, it required to make sense of the data. This can be done by visualisation and the use of standard classical statistical analysis and some amount of data mining. This is why with the help of KNIME it is possible to the blend of algorithms, analytics and visualisation methods to get a better insight into the data at the first look. In KINME Software, it possible to mix and match tools of various open-source projects within a uniformed environment.
- KMINE Software provides the user with all kinds of data analytics methods and functionality, along with the freedom to use the tools on their like and requirement.
- KNIME Analytics Platform helps in ensuring that all the process around the data is explainable and understandable in the sense of building high-quality models and visualisation for the user to or a business to rely on. This helps in retaining the quality and accuracy in your analytics as it helps in model maintenance, helps in fixing easier when there is something wrong.
- KNINE Software helps the users to spend their time where it makes the most impact as there are prebuilt components available to use for automating a certain process and provides the option of faster prototyping and testing of the models.
- With the help of KNIME Server, it is possible to easily deploy and scale your work as a web application or REST service without the worry and need to write a single line of code.
- Deploy & Manage
KNIME Software helps in enabling the user to take their time, team and technology resources in order to meet the needs of the overall team and enterprise. The use of deployment and management functionalities makes it easier to productionise the data science applications and services and deliver a usable, reproducible and reliable insight for the business.
- With the use of KNIME Software, it is possible to lead a happy and productive team as there is the freedom to choose from a wide range of tools for data wrangling, analytics and visualisation in one uniformed environment, by providing of giving them the ability to integrate new technology and tools. This way they can focus on the problems at hand and more importantly that matter.
- KNIME Software helps in standardising processes and establishing best practices which allow to share knowledge and manage a successful diverse team into delivering high-quality results which align with the requirements.
- KNIME Software helps in bridging the gap between data science and business as the software allows the data science team to easily deploy workflows to the business analysts with minimal to no coding requirement. Also, with the use of KNIME Server, the workflow that is deployed as a REST service the business analysts can have access to the analytical applications from their browser.
- There are features like backward compatibility, rollback and granting permissions around workflow present in the software which helps in avoiding unintended changes and mitigating the risks arising in the data science cycle.
- Consume & Optimise
KNIME Software helps in setting the data science life cycle in a manner that the user can provide the feedback or requirement for analysis, where the data science process immediately modifies itself to reflect the changed requirement or new insights.
- With the open and flexible framework of KNIME Software, it is possible to create and deliver actual value and insights for your business. This insight can be via visualisation, applications or service that is required by the user.
- KNIME Software lets the user be a part of the data science process in a highly participatory and collaborative manner. With the software, it is possible to reach back into the data science cycle at a predetermined location or touchpoint of the process to update or tweak the dataset or paraments without majoring touching the actual workflow.
- With the help of KNIME Software, it is possible to mitigate the possible risk related production rising of data science. The software helps in providing documentation, versioning, user right management, and encryption tools to meet business needs. KNIME Software is deployable across all platforms, thus providing an open and extensible framework that allows the user to blend the required tools and skills to meet the changing needs of the business.
KNIME Workspace is a folder on the user’s local computer that stores the user’s workflow, node’s setting and the data produced through the workflow. The workflow and data store in the KNIME Explorer component of the workspace.
There are seven components that make up the KNIME Workbench. These seven components are as follows:
- KNIME Explorer: this component provides an overview or information of the available workflows or workflow groups that currently active in the KNIME workspaces, i.e. the user’s local workspace, KNINE Servers and the user’s personal KNIME Hub Space.
- Workflow Coach: The Workflow Coach provides a list of node recommendations based on the workflows that are built by the community of KNIME users. The Workflow Coach is inactive and remains inactive if the user does not allow KNIME to collect their usage statistics.
- Node Repository: The node in the KNIME Analytics Platform represents an individual task. The node can perform any kind of task, varying from reading and writing data to training models and creating visualisations of the data. The picture below shows a bit of information around the node setup and execution on the workbench.
- Workflow Editor: The Workflow Editor component is a canvas present in the KNIME Workbench that is used for editing the current workflow that is active.
- Description: The Description component provides information on a description of the current workflow or a selected node from the Workflow Editor or the Node Repository.
- Outline: This component provides an overview of the currently active workflow.
- Console: The Console component shows warning and error messages around the currently active workflow execution, indicating what is going on under the hood or process of the workflow.
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