Why embracing low-code is good for data engineers

Are low-code and data engineering like fire and ice?

For data engineers, coding has long been a corner stone of their craft. SQL and Python skills are the foundation for robust data pipelines, optimized data models, elegant transformations and ultimately data quality. The idea of going low-code is counterintuitive. Indeed, many data engineers are increasingly turning to low-code platforms to create more impact with their work.

Let’s see what's happening here. 

The constraints of coding 

Let’s be honest – coding can be deeply satisfying for data engineers. Learning to code properly doesn’t happen overnight and requires endless hours of practice and study. It’s only natural to want to hone those skills on the job. If done well, coding provides room for precision and control – things that are critical when building data warehouses.  

But data engineering also has to face the constraints of today’s business environment. Stakeholders demand more data products in less time. Workload is usually higher than capacity and technical debt piles up frustratingly. It’s easy for the business to get cynical about data warehouses as they are face long wait times. Coding can be especially time-consuming for routine tasks, such as common transformations and workflow definitions, which often require extensive lines of code. Under pressure, code quality can suffer, leading to errors and frustration. A coding-first approach might work well initially, but as the pressure mounts, problems quickly escalate.

Last but not least, data engineers are not just technical resources. A large part of their success is driven by the quality of their alignment with the business. This can take many forms: coaching on how to use data appropriately, helping refine requirements, teaching skills, etc.. These activities are time-consuming and require business and communication skills. If you are constantly busy coding, there is little time left for this critical human interaction. This is where low-code solutions come in.

The evolution from coding to low-code 

Low-code platforms are changing how data engineers approach their work. Platforms like Agile Data Engine offer new ways to significantly streamline code generation for routine and complex tasks. The idea is to automate those elements that are rule-based and don’t need manual intervention. Those are usually time-consuming, routine, and repetitive tasks such as consistent table and view creations, consistent and safe approaches to handling structure or data changes, standardizing attribute transformations (for example key or data hash calculations), and managing workflows. Automation helps keep the development process in line with the data model and project standards while preserving historical tracking with a full audit trail. 
This way of working allows data engineers to follow the classic 80/20 rule: focus on the few most important tasks and let automation take care of the rest. What are the benefits of this? 

Benefits of Embracing Low-Code in Data Engineering 

1. Increased Productivity:

Low-code platforms automate repetitive tasks and simplify complex processes, allowing data engineers to focus on strategic initiatives, accelerating development cycles, and boosting productivity. 

2. Enhanced quality, reliability, and consistency:

Automated features reduce human error, leading to improved data quality and reliability. Consistent data transformations and workflows ensure rigorous application of standards. 

3. Increased trust: 

Faster development cycles enable data engineers to deliver solutions more quickly, enhancing trust between business and IT through agile delivery. 

4. Improved collaboration: 

Standardization simplifies coaching and knowledge sharing. It fosters better teamwork and reduces the effort required to onboard new data engineers or to take over for colleagues while they are out.  

5. Higher job satisfaction:

Shifting from tedious tasks to high-value work improves motivation and job satisfaction. Working in an environment with less technical debt and gaining trust from the business enhances overall job satisfaction. 

Embracing the Shift 

Low-code platforms are democratizing access to sophisticated data engineering capabilities. While the transition from traditional coding methods to low-code platforms may initially challenge the established norms and practices of data engineering, the benefits far outweigh the perceived risks. Embracing this shift allows data engineers to leverage their expertise more strategically, driving tangible outcomes and keeping technical debt at bay. 

Conclusion 

While coding will remain a fundamental skill for data engineers, integrating low-code platforms into their toolkit opens new avenues for creativity, efficiency, and collaboration. Our data engineers at Agile Data Engine recognized the power of low-code back in 2016, finding it rewarding to focus on high-value tasks instead of being bogged down by detailed work. Let us know if you want to learn more and join the shift towards more efficient data engineering practices. Want to see this in practice? The following demo shows how easy it is to make schema & workflow changes without coding.