Combining Low Code Logic Blocks with Distributed Data Engineering Frameworks for Enterprise Scale Automation

Abstract

Integrating low code logic blocks with distributed data engineering frameworks enables
organizations to rapidly assemble and operate large-scale data pipelines with improved automation, modularity,
and execution efficiency. By combining visually configurable logic components with the parallel processing
capabilities of distributed engines, this hybrid model delivers consistent transformation behavior, accelerated
development cycles, and robust system reliability. Metadata-driven configuration further enhances
maintainability by ensuring uniform semantics across workflows, while automated scaling and dynamic
resource allocation help sustain high throughput under diverse workloads. This approach provides a future
ready foundation for enterprise-scale data automation, supporting both operational stability and rapid
adaptation to evolving data requirements.