Formalizing ETLT and ELTL Design Patterns and Proposing Enhanced Variants: A Systematic Framework for Modern Data Engineering
Chiara Rucco, Motaz Saad, Antonella Longo
TL;DR
The paper addresses the lack of formalization for hybrid data ingestion patterns by proposing ETLT++ and ELTL++ as reusable design patterns with explicit data contracts, versioning, and observability. It formalizes ETLT and ELTL within a design-pattern framework, then extends them to enforce governance and reproducibility through contracts, time-travel capable loading, and continuous quality monitoring. Practical artifacts include structured pattern definitions, data-contract schemas, validation rules, and examples illustrating raw/semantic layering and cost-aware raw data management. The work aims to bridge the gap between industry practice and academic rigor, offering a structured, auditable roadmap for building modern, multi-cloud data pipelines with improved quality, lineage, and usability.
Abstract
Traditional ETL and ELT design patterns struggle to meet modern requirements of scalability, governance, and real-time data processing. Hybrid approaches such as ETLT (Extract-Transform-Load-Transform) and ELTL (Extract-Load-Transform-Load) are already used in practice, but the literature lacks best practices and formal recognition of these approaches as design patterns. This paper formalizes ETLT and ELTL as reusable design patterns by codifying implicit best practices and introduces enhanced variants, ETLT++ and ELTL++, to address persistent gaps in governance, quality assurance, and observability. We define ETLT and ELTL patterns systematically within a design pattern framework, outlining their structure, trade-offs, and use cases. Building on this foundation, we extend them into ETLT++ and ELTL++ by embedding explicit contracts, versioning, semantic curation, and continuous monitoring as mandatory design obligations. The proposed framework offers practitioners a structured roadmap to build auditable, scalable, and cost-efficient pipelines, unifying quality enforcement, lineage, and usability across multi-cloud and real-time contexts. By formalizing ETLT and ELTL, and enhancing them through ETLT++ and ELTL++, this work bridges the gap between ad hoc practice and systematic design, providing a reusable foundation for modern, trustworthy data engineering.