Push Down Optimization for Distributed Multi Cloud Data Integration

TL;DR

This paper investigates the application of push down optimization techniques in multi cloud ETL pipelines, demonstrating potential performance and cost benefits despite challenges like data movement and heterogeneous engines.

Contribution

It analyzes the feasibility, benefits, and limitations of push down optimization in multi cloud environments, proposing strategies to enhance scalability and efficiency.

Findings

Lower end-to-end runtime in multi cloud ETL workflows

Reduced data transfer volume across clouds

Improved cost efficiency in distributed data processing

Abstract

Enterprises increasingly adopt multi cloud architectures to take advantage of diverse database engines, regional availability, and cost models. In these environments, ETL pipelines must process large, distributed datasets while minimizing latency and transfer cost. Push down optimization, which executes transformation logic within database engines rather than within the ETL tool, has proven highly effective in single cloud systems. However, when applied across multiple clouds, it faces challenges related to data movement, heterogeneous SQL engines, orchestration complexity, and fragmented security controls. This paper examines the feasibility of push down optimization in multi cloud ETL pipelines and analyzes its benefits and limitations. It evaluates localized push down, hybrid models, and data federation techniques that reduce cross cloud traffic while improving performance. A case study across Redshift and BigQuery demonstrates measurable gains, including lower end to end runtime, reduced transfer volume, and improved cost efficiency. The study highlights practical strategies that organizations can adopt to improve ETL scalability and reliability in distributed cloud environments.