Efficient Data Ingestion in Cloud-based architecture: a Data Engineering Design Pattern Proposal

2025-03-20

AI Summary

To address the challenge of real-time and batch hybrid ingestion of high-throughput, multi-source heterogeneous big data in cloud environments, this paper proposes a metadata-driven data ingestion design pattern. The pattern decouples ingestion policies, schema definitions, and data source configurations, enabling flexible switching between incremental and full-load ingestion, dynamic schema evolution, and zero-code onboarding of new data sources. Built on the Azure platform, a configurable ingestion framework integrates declarative configuration, event-driven pipelines, and a dual-mode ingestion engine—overcoming scalability limitations inherent in traditional hard-coded architectures. Empirical evaluation demonstrates a 42% reduction in average ingestion latency and decreases new data source onboarding time from days to minutes. These improvements significantly enhance data engineering agility and operational efficiency.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In today's fast-paced digital world, data has become a critical asset for enterprises across various industries. However, the exponential growth of data presents significant challenges in managing and utilizing the vast amounts of information collected. Data engineering has emerged as a vital discipline addressing these challenges by providing robust platforms for effective data management, processing, and utilization. Data Engineering Patterns (DEP) refer to standardized practices and procedures in data engineering, such as ETL (extract, transform, load) processes, data pipelining, and data streaming management. Data Engineering Design Patterns (DEDP) are best practice solutions to common problems in data engineering, involving established, tested, and optimized approaches. These include architectural decisions, data modeling techniques, and data storage and retrieval strategies. While many researchers and practitioners have identified various DEPs and proposed DEDPs, such as data mesh and lambda architecture, the challenge of high-volume data ingestion remains inadequately addressed. In this paper, we propose a data ingestion design pattern for big data in cloud architecture, incorporating both incremental and full refresh techniques. Our approach leverages a flexible, metadata-driven framework to enhance feasibility and flexibility. This allows for easy changes to the ingestion type, schema modifications, table additions, and the integration of new data sources, all with minimal effort from data engineers. Tested on the Azure cloud architecture, our experiments demonstrate that the proposed techniques significantly reduce data ingestion time. Overall, this paper advances data management practices by presenting a detailed exploration of data ingestion challenges and defining a proposal for an effective design patterns for cloud-based architectures.

Problem

• Addresses high-volume data ingestion challenges in cloud-based architectures.
• Proposes a metadata-driven framework for flexible data ingestion.
• Reduces data ingestion time using incremental and full refresh techniques.

Innovation

• Metadata-driven framework for flexible data ingestion
• Combines incremental and full refresh techniques
• Tested on Azure cloud, reduces ingestion time