Why Low-Code is the Future of Cloud-Native ETL

As Machine Learning and Artificial Intelligence initiatives continue to expand, the infrastructure supporting them (especially ETL and ELT pipelines) remains foundational. These pipelines are responsible for gathering and delivering reliable data to downstream processes. As the pace of innovation increases, so too does the demand for rapid development and easy maintenance of these pipelines.

Over the past year, I had the opportunity to work on two cloud-native ETL projects that offered an instructive contrast. One was a greenfield effort where my team had the opportunity to define the architecture and tooling from scratch. The other, though recently implemented, was already in production and represented a brownfield environment. Our greenfield solution leaned into low-code, visual development tools wherever possible. The brownfield project, by contrast, relied entirely on PySpark, even for simple transformations.

This choice raised an important question: when cloud-native platforms now provide powerful visual tooling out of the box, why default to fully custom code? The answer lies in understanding where low-code delivers the most value, and where it doesn’t.

Low-code platforms provide more than just a faster way to build data pipelines. They also enable better collaboration, improved maintainability, and closer alignment between IT and business needs. These advantages are particularly impactful in environments where time-to-market is critical.

Some key benefits of low-code ETL approaches include:

• Visual development interfaces that accelerate pipeline creation and reduce boilerplate

• Empowerment of non-technical users, like data analysts, to build or modify pipelines

• Built-in monitoring and operational visibility with minimal configuration

• Business-driven workflows that reflect real-world processes rather than abstract code

• Integrated data lineage for traceability, compliance, and debugging

These capabilities are increasingly accessible through native cloud services. For example:

• Azure Data Factory (ADF) enables low-code development, orchestration, and monitoring all within the Azure ecosystem.

• AWS Glue Studio provides a visual interface on top of Spark, making it easier to build ETL jobs without deep Spark expertise.

A real-world illustration of these benefits is highlighted in SPR’s work with a national health services company. In that engagement, SPR leveraged AWS-native tools to modernize legacy applications and streamline data flows. By integrating cloud services (like AWS Lambda, Glue, and RDS) into a unified architecture, the team enabled faster deployment cycles and improved maintainability. This kind of platform-native, low-code-enabled architecture is precisely the model that supports rapid innovation in data and AI initiatives.

That said, low-code isn’t ideal in every scenario. There are cases where traditional coding remains the better, or only, option.

Situations where low-code platforms may fall short include:

• Highly complex transformation logic that doesn’t translate cleanly into visual components

• Extensive code reuse needs across many pipelines, which low-code platforms may not support well

• Gaps in native connectors, especially for niche or legacy data sources

• Scalability or performance tuning requirements where abstracted logic limits optimization

When organizations encounter limitations with legacy tools and fully coded ETL scripts, they can adopt cloud-native and hybrid strategies. Success comes from finding the right balance between reusable code and maintainable, visual workflows, often with native support from cloud platforms like Azure and AWS.

In our greenfield project, we adopted a hybrid approach that blended low-code development with targeted use of custom logic. The result was a solution that met performance and complexity needs without sacrificing maintainability.

Here's how we structured it:

• Low-code visual pipelines were used to orchestrate the overall flow, making them accessible to analysts and easier to troubleshoot.

• Stored procedures encapsulated complex logic, owned and maintained by IT developers.

• We intentionally avoided PySpark in this case, since our team already had deep familiarity with SQL and wanted to reduce onboarding friction.

This separation of concerns allowed different roles (developers and analysts) to work within the same system, each using tools aligned to their skill set. It also improved maintainability and made knowledge transfer easier across teams.

In contrast, the brownfield project, though technically robust, was encumbered by its reliance on custom PySpark code. Even minor modifications required developer involvement and deep system knowledge. The absence of visual pipeline representation made troubleshooting and onboarding significantly harder.

Successfully implementing a hybrid ETL model requires some planning. Below are some guiding principles that proved valuable in our experience:

• Modularize custom logic: Encapsulate transformations in stored procedures or UDFs to reduce duplication and improve testability.

• Use consistent interfaces: Standardize how data flows into and out of these modules to reduce complexity in the visual orchestration layer.

• Enable CI/CD: Source control both your pipeline definitions and custom code assets, ideally with deployment automation.

• Unify monitoring: Ensure you can trace, log, and alert across both the visual and code-based portions of your stack.

SPR takes a pragmatic approach to designing and delivering these hybrid architectures. As described in our approach to digital transformation, we focus on aligning business needs with scalable technical solutions, often building side-by-side with client teams to develop systems that are sustainable, not just functional.

Maintainable, scalable, and adaptable ETL pipelines are critical enablers of successful AI and ML initiatives. While low-code platforms won’t replace the need for custom development in all cases, they offer significant value, especially when quick delivery and operational simplicity are priorities.

When paired with traditional code in a hybrid strategy, low-code tools can offer the best of both worlds: speed and flexibility without compromising control. As cloud-native platforms continue to mature, this balanced approach is one we believe more organizations should consider, and we’ve seen firsthand how effective it can be.

If you’re exploring how to accelerate your ETL pipelines, modernize legacy data flows, or better align your data architecture with AI/ML initiatives, let’s talk. We’ve helped organizations across industries build for the future, and we’re ready to help you do the same.