KNIME Analytics Platform Reviews

Quick to production ML models.

Easy to learn and start delivering.

Great integration with powerful programming languages (Python, Java) .

One stop shop for production ready Data Engineering/Data Analytics and Data Science solutions.

Inbuilt Version control can be a great addition.

Compatibility check for workflows between Client and Server versions can be great for development.

A bit spacious UI for the client application would be appreciated.

ML model deployments (easy and quick) .

No code ETL process (easy to learn) .

Cheaper solution compared to alternatives.

One stop analytical application for integrating ETL, modelling and reporting.

الفائدة العضيمة التي كان عليها التعاون بين البلدين في ألعالم من اجل تحقيق الاهداف الاستراتيجية التي تعمل على تطوير التعاون المشترك بين دول ألعالم

أنها لم يكن هناك ما يشير الي وجود بعض الدول التي لا تزال تعيش في مجال

المعرفة

الفائدة العضيمة التي كان عليها التعاون بين البلدين في ألعالم من اجل تحقيق الاهداف الاستراتيجية التي تعمل على تطوير التعاون المشترك بين دول ألعالم

أنها لم يكن هناك ما يشير الي وجود بعض الدول التي لا تزال تعيش في مجال

المعرفة

It is a user-friendly and versatile data analytics tool that offers a wide range of data analysis and visualization capabilities. It has an extensive collection of pre-built nodes that can be customized and connected to create complex workflows

It provides various visualization tools, but sometimes available options are limited compared to other data analytics platforms. Sometimes it s challenging to work with unstructured data, such as text or image data.

It allows the integration of data from various sources and formats, such as databases, spreadsheets, and cloud storage, which can save time and effort and help to streamline data processing workflows. It also provides various machine learning and data analytics tools that can help to build predictive models, perform statistical analysis, and gain insights from their data.

Easy low/no-code platform

Open source

Easy to use for beginners

Lack of learning resources

Consumes a lot of memory

UI interface is not very navigable

-Good Tool for solving data cleaning issues

-No license fee

-reduction in manual data wrangling due to ease of automation

Everything.

ETL development is straightforward with Knime.

The learning curve is fast, and we can deliver results to the stakeholders in a few hours.

Without the Knime Server, it's hard to schedule ETLs pipelines. We need to do some workarounds with bash to execute the pipelines properly.

Data migration, extract, transform and load data from one point to another.

(sheets, CSV, databases)

Very user-friendly, has immediate results, and is easy to use. Data engineering, science and visualization tools.

Some challenges while solving the problems. R&D for the unique cases shall be difficult.

Solving data science problems with plug-and-play activities. Drag and drop; it is like a no-code platform

A lot of nodes ans extensions, easy to use and powerful.

Heavy, need high spec memory to run this application.

Use this application for machine learning, programming language not required such as R or Phyton

Data analytic without programming language.

It is a tool from start to finish from file management, through statistics, machine learning, deep learning, selenium rpa, artificial intelligence, process automation, databases and ETL, integration with Python and R, advanced analysis, teamwork, parallelization, real big data; great

nothing , is a good tool, the ability to process video in real time

the time that has been saved on artificial intelligence and analytics issues has been quite a lot compared to traditional tools, if the tool is handled properly to a large percentage the results are incredible

I have solved from inventory delivery problems, to neural network optimization, the time I have been able to save is too much compared to pure programming

Great tool for predictive analytics. The examples provided with the software are a great way to learn this tool by your self.

It is really difficult to prepare and clean data.

It is great for machine learning.