Member-only story
Low Code Tools are replacing Traditional Data Engineering Tools
In the fast-paced data engineering domain, the emergence of low-code tools has been met with mixed feelings.
These tools, often maligned by traditional data engineers, have gained popularity among businesses for their promise of accessibility and efficiency.
In this article, we’ll explore what low-code tools are, why they are in demand, and whether they truly live up to the hype.
What is a Low Code Tools ?
Low code tools, often referred to as no code tools, aim to simplify the process of building and maintaining data pipelines.
The term “low code” is a more accurate representation as most of these tools incorporate some level of coding or expression language to enhance their basic functionalities.
The allure of these tools lies in their drag-and-drop interface, promising a quick and easy way to create data processes without extensive coding.
The Promise
The marketing pitch for low code tools is enticing. They promise a world where anyone, including analysts without extensive coding experience, can effortlessly build and maintain data pipelines.
The appeal is not just about ease but also about reducing the need for expensive engineering teams, fostering collaboration, and empowering analysts to handle their data tasks independently.
To understand the impact of low code tools, it’s essential to clarify the distinction between coding, programming, and development.
Coding is about converting logic into machine-readable instructions, while programming involves the analysis and conceptualization of logical structures to solve a problem.
Development, on the other hand, encompasses the entire process, including design, documentation, monitoring, testing, and maintenance.
The promise that “anyone can do it” with low code tools oversimplifies the complex nature of building scalable and robust data pipelines.
The Reality Check
Despite the marketing claims, low code tools do not eliminate the need for skilled engineering and development…
