Last updated on Nov 07, 2023
Alteryx is one of the popular self-service data Analytical tools. This helps the business users to build their data workflows quickly and faster preparation of data blending without any programming skills. We can perform many tasks using the Alteryx tool such as drag, a drop of workflow, and cleaning of data. These tasks enable business users to produce the data output quickly and effectively by analyzing the data input. Alteryx is an American computer software company based in California with a development center in Broomfield, Colorado. These company products are mainly used for data science and Analytic purposes.
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We must say section is a tricky one to explain, but we are going to compare these two BI tools based on these categories such as;
*Note: Both Alteryx and Knime tools use teh same workbench approaches.
Let’s start explaining them one by one,
First start with Alteryx
The Alteryx tool’s dashboard is similar and the data nodes are grouped easily to understand the things from the menu toolbar to the on the top of the application. The nodes are arranged into various options they are color-coded categories that include In/Out, data preparation, Join, Parse, data Transform, reporting, and more. With the Alteryx tool, each configuration setting is embedded into the node. Clicking on the node immediately opens up the interface, which can be expanded or closed as per user requirements. With Knime, Maneuvering between the node is very easy when compared to the Alteryx. But one major drawback with Knime is that you may end up with a large number of extraneous windows all over the place, which consumes a considerable amount of memory and this also slows down the operation.
When it comes to Knime, users can view the list of nodes in the repository that is divided into segments. Each repository node can be dragged onto the canvas and can be connected by dragging a line from an output to an input of another data node. By clicking on the right side you can see the complete configuration settings of Knime.
The primary purpose to use these ETL tools is only for the data preparation operations. For both the Alteryx and Knime users can pull the data from multiple locations, they are ranging from CSV files, databases, or any cloud resources.
Now it’s time to know the major differences between Knime and Alteryx:
Alteryx provides multiple screens that enable you to drag and drop, and also the selection of the data types in a visual way. Once you get connected an input/output tool to a database or a file we can automatically view the quick visualization that contains the complete details about each data point contained. Compare to Knime, the Alteryx data tool is quite clear and easy to understand also modifying the data types is also easy and one can use the drop-down to select types.
While using Knime, just by double-clicking on a node we are given access to the various configuration options.
In the below image, you can able to see the options like file reader, and opening a CSV file.
When loading a file in Knime, there is an option available that is a preview of data in the file and the ability to modify it including types and more. Knime offers the ability to filter not only rows but also column data which can be useful when you can have a large range of primary keys in a database or spreadsheet.
Both knime, as well as Alteryx, provide strong features for data combining, data cleaning, and data preparation process.
Now the major differences between Knime and Alteryx:
First start with Alteryx:
Alteryx’s join tool enables users to choose a simple identifier (such as UserID, which is shown below) and use it to link together various databases. The joint tool in Alteryx also allows us to construct SQL queries where no coding is needed. One of teh huge advantages of using the Alteryx tool is that the data itself is not completely transferred into the new file before configuring the connections.
The below diagram illustrates the overall data connectivity in the Alteryx:
Whereas Knime’s join tool is easy to understand and also easy to use for the multiple combing data sets, ability to easily filter data from each data set and can decide whether or not we want to include or exclude it in the eventual database servers.
The following figure illustrates the data blending or data connectivity process in Knime:
To perform the data analysis process, python is considered to be one of the powerful tools among others. Now let’s list out the primary differences between Knime and Alteryx based on Data Analysis;
Alteyx consists of a few useful resources such as data investigation tools, Pearson, and spearman correlation. Alteryx’s data analysis feature allows users to load various analytical models, and also built-in scoring models for predicting values, data set classes, and this can be used for scoring new models. One major drawback is that, unlike Knime, Alteryx does not offer the same range of data analytic tools.
Knime is considered to be a strong tool this is because it includes various data analytical tools. Knime is an open-source tool so hundreds of developers prefer to use this one to create a wide range of plugins and adapters that enable us to use many pre-existing functionalities. Knime also includes pre-existing ML (machine learning) style tools to allow existing from existing models and to create new predictive models. It can handle various regressions and also assist in building decision trees, and some model evaluations. Knime includes an exceptionally wide range of tools.
As we know that both Alteryx and Knime are ETL tools. Once done with the data extraction and transmission of data, the next step is to load or output the data into the section of standardized formats. Now let’s see the major differences between Knime and Alteryx related to the output category.
To perform data outputting tasks, the Alteryx offers an easy-to-use “output data tool” that will connect to a vast array of different data sources and file formats. This functionality includes standard CSV flat file-like options to SQL and non-related databases as well as a number-one cloud-based data formats from top companies like Google, Amazon, and Microsoft.
The following image explains the subset of the output formats that you can use with the Alteryx tool.
When it comes to the Knime tool, you can export your data into a standard CSV file for working with later. You can also export your information into a BIRT (business intelligence reporting tool) report that is similar to Tableau hyper file.
Example of BIRT report:
Knime also makes it possible to export data into a wide range of databases and formats ranging from MS SQL Server, MYSQL, PostgreSQL, and many more.
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The alteryx tool includes a wide range of graphics, however, the Alteryx tool is not designed to perform only this task so you may still think that virtualization with Alteryx is poor.
The following map explains this;
Being an open-source tool that boasts a wide range of charts and graphs that can be generated and they are very easy to create.
Here is the bar chart that explains overall reporting;
The cost differences between these tools are large -Alteryx typically starts at around $3000 annually, while KNIME is free.
First, let’s talk about the primary benefits of the Alteryx:
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Disadvantages of Alteryx:
Below are a few disadvantages of Altetyx, they are;
frequently asked AlterYX Interview questions and Answers !
It is very important to know the advantages and disadvantages of Knime:
Advantages of Knime:
Below are the major advantages of Knime:
Disadvantages of Knime:
The below are drawbacks of the Knime:
[ Related Article: alteryx training ]
Both Alteryx and Knime are powerful ETL tools that will serve the functionalities of many marketing operations. Choosing the best tool is quite competitive because it all depends on the users, and what are all the operations you are going to perform. Both Alteryx and Knime take a slightly different approach to the ETL process. Knime is completely modular, which is separate whereas Alteryx has included some higher level-functionality, by combining tools that make sense to work together by an analyst or experts.
This is the post we hope that you got a complete idea of both the popular and most widely used data analytic tools. In this Alteryx VS Knime, we have tried our best to elaborate the things in a possible way. Users can choose the best ETL tool to perform data analysis and visualization operations that are completely based on the requirements. Most of our Alteryx related blogs help a few data analysts, Alteryx communities, and experts across the world to explore and enhance their knowledge of the data visualizations.
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As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.
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