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Advanced Data Engineering: Orchestration, Governance, and Quality Assurance in Large-Scale Systems
Subject area: Science,Engineering and Technology · Area of research: Financial Technology
Abstract
In today?s data-intensive enterprises, information underpins competitive advantage, realtime operations, and continuous innovation. Modern challenges go beyond storing and retrieving data ? they require designing robust, resilient, and future-ready data infrastructures. This article examines three critical pillars of advanced data engineering: data orchestration, data governance, and data quality assurance. We analyze how orchestration frameworks (e.g. Apache Airflow, Prefect) automate complex pipelines; how governance paradigms (including Data Mesh and data contracts) enforce ownership, policy compliance, and decentralization; and how quality tools (such as Great Expectations and AWS Deequ) embed validation logic to ensure reliable data. By synthesizing current literature and industrial best practices, we propose an integrated framework for scalable, CI/CD-enabled data architectures. A real-world case study in the retail sector illustrates dramatic improvements in system uptime, compliance, and trust in analytics. Finally, we discuss emerging trends (AI-driven observability, self-service governance, etc.), ethical considerations (privacy, fairness, accountability), and recommend future research directions in adaptive automation and policy-driven engineering.
Keywords
Data Engineering; Data Orchestration; Data Governance; Data Quality; Data Mesh; Apache Airflow; Great Expectations; Data Contracts; Self-Service Data; AI Observability; Metadata Management; Continuous Validation.
How to cite this paper
@article{1708390,
author = {Mehul Sharma},
title = {Advanced Data Engineering: Orchestration, Governance, and Quality Assurance in Large-Scale Systems},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {9},
pages = {538-541},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708390.pdf},
abstract = {In today?s data-intensive enterprises, information underpins competitive advantage, realtime operations, and continuous innovation. Modern challenges go beyond storing and retrieving data ? they require designing robust, resilient, and future-ready data infrastructures. This article examines three critical pillars of advanced data engineering: data orchestration, data governance, and data quality assurance. We analyze how orchestration frameworks (e.g. Apache Airflow, Prefect) automate complex pipelines; how governance paradigms (including Data Mesh and data contracts) enforce ownership, policy compliance, and decentralization; and how quality tools (such as Great Expectations and AWS Deequ) embed validation logic to ensure reliable data. By synthesizing current literature and industrial best practices, we propose an integrated framework for scalable, CI/CD-enabled data architectures. A real-world case study in the retail sector illustrates dramatic improvements in system uptime, compliance, and trust in analytics. Finally, we discuss emerging trends (AI-driven observability, self-service governance, etc.), ethical considerations (privacy, fairness, accountability), and recommend future research directions in adaptive automation and policy-driven engineering.},
keywords = {Data Engineering; Data Orchestration; Data Governance; Data Quality; Data Mesh; Apache Airflow; Great Expectations; Data Contracts; Self-Service Data; AI Observability; Metadata Management; Continuous Validation.},
month = {March},
}