A Comparative Guide: RPA Using Python vs. Low-Code/No-Code Tools Like UiPath
As businesses strive to streamline operations, Robotic Process Automation (RPA) is transforming business operations by automating repetitive tasks and freeing human resources for more strategic endeavors. There are two main approaches for creating and managing bots: Python-based RPA platforms and low-code/no-code platforms like UiPath. Python excels in scenarios requiring complex data manipulation, integrations with third-party APIs, and bespoke process automation tailored to specific business needs. UiPath, on the other hand, is a prominent robotic process automation platform designed with a user-friendly interface that enables users with minimal programming knowledge to automate workflows effectively.
This blog will explore the differences between these approaches, highlight their benefits and challenges, and provide insights to help you choose the right one for your business needs.
Companies are moving toward RPA to eliminate human errors, improve productivity, and free up time for strategic tasks. There are different types of RPA tools available in industry, for successful business transition with RPA and to achieve better ROI, selecting right tool is important based on factors like cost, customization needs, skill set availability, timelines, ease required for RPA tool changes in future etc.
Python-based RPA solutions are popular for their flexibility and customization capabilities. Conversely, low-code/no-code platforms like UiPath provide a more user-friendly, drag-and-drop interface. This blog explores these two approaches and their importance in modern automation solutions. Because
How RPA Using Python Works?
This approach requires programming knowledge but offers greater customization and control over processes. Using libraries such as Selenium for web automation, Pandas for data manipulation, and PyAutoGUI for desktop automation, Python enables the creation of highly complex, tailored automation solutions. You can either develop standalone Python solutions by incorporating widely available Python packages along with RPA-specific packages like rpa and rpaframework or opt for Python-based RPA platforms like BotCity and pythonrpa, which also provide ecosystems for monitoring and managing RPA bots. The latter is preferable, as standalone solutions can be challenging to scale, manage, and avoid silos.
How RPA using Low-Code/No-Code (UiPath) Works?
Pros & Cons of Python-based RPA tools
Pros of Python-based RPA | Cons of Python-based RPA |
Python enables the development of highly tailored workflows that can seamlessly interact with APIs, databases, and other external systems. | Implementing Python-based RPA necessitates skilled developers, potentially raising the initial setup costs. |
It is extremely scalable and capable of managing complex tasks such as natural language processing and data science models. | Creating an automation process with Python may take more time compared to using a low-code platform. |
Python’s wide range of open-source libraries offers a budget-friendly solution. | Regular updates and debugging are essential to ensure the smooth operation of scripts. |
Python boasts a large and active developer community, providing abundant resources, libraries, and support for RPA initiatives. | Debugging bot failures becomes challenging as it depends on custom error logging and exception handling. |
Migration has less overhead as you can always reuse the Python scripts in any other RPA platforms | Implementing security features and keeping them updated can become an overhead |
As there is less overhead of intermediate wrappers, it is comparatively faster | Monitoring and maintaining Python-based bots could be challenging |
Pros & Cons of Low-code/No-code RPA tools
Pros of Low-code/No-code RPA | Cons of Low-code/No-code RPA |
Citizen developer with no coding expertise can swiftly create and deploy automation without needing a coding background. | Although UiPath offers pre-built functionalities, it may not provide the same level of customization as Python. |
For common use cases, accelerates the development and deployment process with its ready-made components. | Licensing fees for platforms like UiPath can accumulate, particularly for larger implementations. |
The platform provides numerous built-in integrations with enterprise tools such as SAP, Oracle, and Salesforce. | Dependence on its proprietary ecosystem can sometimes restrict flexibility which makes migration very difficult. |
Some platforms offer services, taking care of the monitoring, maintenance, and updates. | Finding candidates with specific tool expertise can be challenging, resulting in fewer options to choose from. |
Best Practices for Choosing Between Python RPA and low-code/no-code tools
Evaluate Business Requirements: For simple, repetitive tasks, a low-code/no-code tool is often a quicker and easier solution, while Python is better suited for more complex workflows.
Skill Availability: Organizations with in-house developers may benefit from Python as it also gives cost benefits, whereas those lacking coding expertise should consider low-code/no-code.
Scalability: Python offers greater flexibility for scaling complex automation projects, while low-code/no-code is efficient for expanding user access.
The choice between RPA using Python and low-code/no-code tools like UiPath fundamentally depends on the specific needs and capabilities of an organization. Python offers unparalleled flexibility, scalability, and customization, making it a powerful choice for complex automation tasks, while UiPath, with its intuitive drag-and-drop interface, caters to business users with minimal technical skills, enabling rapid deployment.
MosChip provides comprehensive RPA solutions that are suited to your specific business requirements. Whether you require fully customized Python automation or a more streamlined approach using low-code/no-code solutions such as UiPath, our experts can guide you through the implementation process to maximize your automation potential.
Toral is a manager at Softnautics and has total of 11+ years of experience in quality engineering of Embedded Systems and DSP software platforms. In her career, she has worked on numerous QA and Automation projects, test framework development, and DevOps projects. She is passionate about achieving optimum process automation and developing productivity improvement tools. While not working she likes to travel and read.
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