Designing Scalable ETL Pipelines for Multi-Source Graph Database Ingestion

Abstract

As organizations seek to harness complex, interconnected datasets from diverse sources, graph databases offer powerful capabilities for relationship-centric analytics. However, ingesting multi-source data into graph structures introduces challenges in schema reconciliation, semantic consistency, scalability, and real-time processing. This paper presents a systematic study of scalable Extract, Transform, Load (ETL) pipeline design tailored to graph database ingestion. It synthesizes architectural principles, transformation strategies, and scalability paradigms distinguishing between traditional relational ETL and modern graph-centric approaches. Drawing from a comprehensive literature review and thematic analysis, the paper identifies critical challenges such as schema heterogeneity, high-velocity data streams, and incremental change propagation. Best practices are proposed for data quality management, fault tolerance, modular pipeline design, and hybrid (batch + streaming) ingestion. The study highlights the trade-offs between open-source and proprietary ETL solutions and outlines future directions involving AI-driven automation and metadata-driven orchestration. These insights aim to guide both researchers and practitioners in building robust, flexible, and high-performance ETL systems for dynamic graph data ecosystems.