Alteryx VS Python

What is Alteryx:

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 Alteryx 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

Become a master of Alteryx Tools by going through this HKR Alteryx Training!

What is Python:

Python is a widely-used programming language; this supports procedure-oriented as well as Object-oriented programming. Python is considered a minimalist language. There are lots of advantages of using this language, few of them are simple to use, compatible, and availability of open-source libraries. It also helps in the analysis of large data through its high-performance features. Python is widely considered the preferred language for learning machine learning. Most of python’s features allow us to perform data handling and data manipulation in a greater way. Python programming codes enable us to think like a programmer and not waste time with confusing syntax.

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Difference between Alteryx and Python :

We feel that this is an important part of this post, let’s start learning the major differences between Python and Alteryx.

Firstly we would to mention the key benefits/ differences of the Alteryx.

1. Alteryx is a platform that can act as a single source of truth, not to address the business problems.

Alteryx has limited in-capabilities to address the industrial supply chain problems and optimization models. As a platform to address business problems, Alteryx is not the right choice as it does not have in-built frameworks to directly approach pre-solutions.

2. Fast and successful data analytics:

Implements a series of analytical sequences for combining and processing a plurality of data sources.

3.Alteryx platform for clearest data analysis:

This platform consists of interfaces, that allow easy access to analysis and data. It is a spatial and non-spatial data interface combined to create analytical processes and applications. It is also very successful to analyze and visualize the data.

4. Time-saving, simple, and complete graphical representation for analysis.

The Alteryx solution allows us to prepare our data in order to plan marketing studies, commercial, or diversity-specific services for our customers. 

5. Great alternatives to excel when reviewing a large amount of data.

Alteryx is best used for handling large sizes of data which excel is not capable of.  It is a brilliant tool with great visualization that deals with SQL codes in the background.

6. Alteryx analytics - Bringing supply chain together.

The tools enable bring all the aspects of supply together and make your decision making way easier. You can create multiple scenarios to ensure effective business decisions are taken.

7. Combining Alteryx desktop, scheduler, and Tableau to create Actionable supply chain metrics.

Great data preparation tool to run data through supply chain databases. 

8. Easier data manipulation process:

Alteryx is incredible at manipulating data to get it what we want, and it's absolutely the problem solver.

Want to know more about Alteryx, visit here Alteryx Tutorial.

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Now it’s time for us to know the Major benefits/ differences of Python:

1. Great for machine learning :

Python is a great language and has a great community. Most of the modern machine learning and deep learning frameworks use Python as their main language. 

2. Python is simple and elegant:

Python offers simplicity, ease of use, portability, and different comparative ventures in which it ends up being a very versatile and practical programming language. 

3. Locust (Load testing tool) using python:

Locust (load testing tool) is a testing tool written using the Python language. It is distributed and scalable as compared to other open-source testing tools. With the help of Python, we can also write API tests, integration tests, and create test scripts.

4. Python is as efficient and comfortable as reptile Python.

It is a great programming language, many IT people in the IT sector have already learned this language because of its convenience that anybody can learn it without much time also. Great to work with Python. It is very easy to go through our code and does not take much time and endeavor.

5. A Large number of packages/libraries available:

There are a lot of python packages available that we can integrate with our test framework to automate the complete workflow. Python language is very easy to code and fast to code. With the help of a wide userbase, we are able to solve any issues faced during coding and also get to know the best way to write good code in Python.

6. Speeds up the code writing process:

The syntax, that is without the unnecessary significant characters for different code blocks or different rows is genius. This has two impacts, first, it speeds up the code writing process because you don't have to insert difficult characters, secondly, it ensured the style of the code is better.

7. Creating a model is easy and effective:

Creating a model with this powerful tool is easy and effective this is because of its robustness.

8. Availability and adoption:

Availability of a huge amount of additional modules supporting scientific calculations and machine learning. Excellent adoption from the community allows users to find the solutions to many tasks in the discussion groups. 

9. Compact code usage:

Python codes are very compact to use when compared to other programming languages. It is a dynamic data type and fully loaded with built-in functions so you only need to know how to use it rather than focusing on implementation.

10. Easy to learn and implementation:

Python is very easy to learn and implement due to most of the inbuilt functions which are easy to call, faster execution, and platform-independent.