Declarative Data Pipeline for Large Scale ML Services

TL;DR

This paper introduces a Declarative Data Pipeline architecture that enhances large-scale machine learning workflows by improving efficiency, scalability, and maintainability through modular design integrated with Apache Spark.

Contribution

It proposes a novel declarative framework with logical computation units called Pipes, enabling scalable, maintainable, and efficient ML data processing in distributed environments.

Findings

50% improvement in development efficiency

Collaboration efforts reduced from weeks to days

500x scalability and 10x throughput gains

Abstract

Modern distributed data processing systems struggle to balance performance, maintainability, and developer productivity when integrating machine learning at scale. These challenges intensify in large collaborative environments due to high communication overhead and coordination complexity. We present a "Declarative Data Pipeline" (DDP) architecture that addresses these challenges while processing billions of records efficiently. Our modular framework seamlessly integrates machine learning within Apache Spark using logical computation units called Pipes, departing from traditional microservice approaches. By establishing clear component boundaries and standardized interfaces, we achieve modularity and optimization without sacrificing maintainability. Enterprise case studies demonstrate substantial improvements: 50% better development efficiency, collaboration efforts compressed from weeks to days, 500x scalability improvement, and 10x throughput gains.