10 reasons to adopt no-code visual ETL solutions

Software technology aims to help people do more with less. So, why should developers be an exception to this rule? 

Hand-coding data pipelines, regardless of the language or platform, is a time-consuming, largely manual, and highly technical process. Challenges around scale, performance, and quality assurance persist, especially when teams comprise varying skill levels. 

Moreover, data engineers often have short tenures, leaving behind the proverbial Jenga tower for their successors to maintain and clean up as the tower builders move to their next job. Those who remain at a company for a long time sometimes build mountains of career-safe code. I have spent a lot of days and nights dangling from the cliffs of such mountains.

As someone with a technical background, I find that the older I get, the more time I want to spend uncovering the truths inside the data and finding solutions that move the needle for my stakeholders, rather than wrestling with syntax issues. I’m not proud. 

1. Speed of development

Low-code/no-code solutions can turn the often tedious process of building and deploying data pipelines into a (fun?) walk in the virtual park. Drag and drop a few widgets, enter your credentials, and you’re off to the races. 

Even the most well-stocked code libraries require time to sift through. And once a script is "tailored," it’s only as good as the engineer behind it—the one that left the company a year ago.

Quality assurance takes on a new role. It now focuses on answering the last-mile question, “Are the outputs correct?” rather than first-mile headaches like “Does the pipeline code even work?” or “Did anyone break the code in the last update?” This is a massive accelerator.

2. Ease of use

These platforms are designed to be as user-friendly as possible, allowing even those whose coding skills are nada to create and manage data pipelines. 

Since the learning curve is practically a flat line, your data becomes accessible to a broad range of data workers who might not code but are wizards in using applications, and the elimination of developer bias can actually be an advantage. 

And BTW, skilled developers aren’t excluded—they just find new ways to flex their muscles. Architecture is still a thing. But these folks’ biggest value is understanding the data itself, the sources and the business motives, and finding the right questions that need to be asked.

Also, If a platform supports code injection, whereby you can slip in high-code snippets and libraries into a no-code pipeline, everyone can contribute at their highest level, from Padawan to Jedi Master.

I once worked on a data engineering team of 20, coding everything by hand. We had a guru, “Master Doug,” who had to manually verify every last line of data code. His schedule? Packed with 30-minute meetings all day, every day. Sometimes, it would take a week just to get on his schedule. 

Talk about a bottleneck; it was like waiting for Gandalf to bless your code just so you could do a commit.

3. Reduced complexity

No-code solutions take the complexity out of complex tasks and workflows, making it easier to manage and maintain data pipelines. Canvas-based pipelines are self-documenting, and if the platform supports auto-documentation via GenAI, then all of the un-fun stuff is handled.

Side note: Putting that process in reverse is the holy grail—typing out “this pipeline handles X, Y, and Zed”, and having the platform make it so; this is the kind of “coding” I think we can all agree with. And, actually, it’s a thing.

4. Cost efficiency

By slashing development time and reducing the need for specialized skills, low-code/no-code solutions can lower overall costs. Ramp-up time for new data engineers is fast, productivity shoots up, and development estimates are more accurate, leading to fewer cost overruns from projects that just… drag… on...

5. Improved collaboration

These platforms often include features that enhance collaboration between the technically savvy and the not-so-tech-savvy. 

Pipelines become easier to understand for all team members, making critical data processes more “explainable.” As such, high-tech and low-tech skill levels can contribute harmoniously to the same pipelines at their highest potential.

6. Iterations and rapid development

Rapid prototyping and iterations are possible, allowing for quick adjustments and improvements to data pipelines. 

In the hand-coding world, prototyping is often reserved for senior developers, but with no-code platforms, everyone can get in on the action, from the intern to the CTO. This is one of the key enablers of the aforementioned advantages in terms of estimating projects.

7. Automated system maintenance

Many low-code/no-code solutions offer automated maintenance and updates, reducing the burden on IT teams. SaaS platforms ensure there’s no provisioning, maintaining, or hardening of hardware or VMs. 

Redundancy and SLAs are just part of the platform. Waiting on IT services can feel a little like the line at the DMV, and that typically goes away as SaaS ETL platforms are implemented.  

So, not only does this make the systems very easily maintainable, but also reduces a number of roadblocks to implementing a solution or even a new project within a company. 

8. Scalability

These solutions are designed to scale effortlessly with growing data volumes and business needs. Plus, the no-code components are regularly updated by the vendor to leverage new technologies and optimizations. 

The expectation is that no-code platforms are highly scalable. If something isn’t up to par, the burden of fixing it shifts to the ETL vendor—because sometimes, it’s nice to let someone else handle the heavy lifting.

Once upon a time (2010s), I was at a small, data-centric company where our business-critical pipelines often faced issues with performance of ingestion and replication processes. I led a team that included two developers who did nothing but debug and architect solutions for these issues; our combined salaries were around $400k per year. 

That’s a hefty sum to spend on problems that no-code solutions have already solved. We could have thrown those dollars at a solution that would make the entire team more productive instead. Ahh, hindsight…

9. Integration capabilities

Low-code/no-code platforms often come with pre-built integrations for various data sources and destinations, simplifying the integration process. 

While high-code tech can handle API discoveries and documentation, no-code ETL vendors offer pre-built interfaces that are not only compatible but also contextual to business needs. 

Smart vendors also look at how customers use their connectors and continually enhance and align them toward business value.

10. Long-term vision

Yep, I said it, and I mean it. No-code and low-code platforms set the pace at which your company extracts value from its data. They are faster to implement and more robust to operate. 

What you might lose in flexibility is easily regained by using code injection capabilities and leveraging automation, ease of use, team-based data optics, and rapid development and implementation.

I’ve seen many a rip-and-replace of no-code solutions, but no more often (and no more difficult) than a major code refactor or technology shift. Change is the only constant, and I don’t think there’s a winner here. But with no-code solutions, you get all the other benefits.

Summary

Data engineering isn’t going anywhere, but its nature is evolving. High-code solutions are beginning to resemble walkie-talkies in the age of smartphones—nostalgic, but not very practical because high-coding is flexible but also carries a ton of baggage. 

Automation is the expectation, even among developers, for just about everything, but it’s been a hard road to hold our development practices to that same standard. 

There are always going to be coders. After all, “no-code” is just someone else’s code. But there are a lot of us, coders and non-coders alike, whose real value might be spending more time looking for the gold nuggets in the data.