Data Engineering Challenges in Multi-cloud Environments: Strategies for Efficient Big Data Integration and Analytics
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The exponential growth of data and the rising demand for scalable, resilient, and cost-efficient computing resources have driven many enterprises to adopt multi-cloud strategies—leveraging services from multiple cloud vendors such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). While this architectural shift offers numerous benefits including flexibility, vendor independence, and improved fault tolerance, it also introduces significant challenges for data engineering teams tasked with building and maintaining robust data pipelines.
This paper provides a comprehensive exploration of the core data engineering challenges in multi-cloud environments, including data integration complexity, increased network latency, fragmented governance protocols, and difficulties in achieving unified observability. Through a structured examination of current literature and industry practices, the study reveals how heterogeneity in cloud architectures creates barriers to seamless big data operations and real-time analytics.
In response, the paper proposes a set of strategic frameworks and technical approaches that enable efficient big data integration and analytics across cloud boundaries. These include the adoption of containerized orchestration platforms (e.g., Kubernetes and Apache Airflow), metadata registries (e.g., Apache Atlas), data lakehouse architectures (e.g., Delta Lake, Snowflake), and federated query engines. The paper also evaluates the performance and adaptability of leading ETL tools—such as Apache NiFi, AWS Glue, and Talend—through a comparative analysis supported by tables and performance graphs.
Real-world case studies, including those from Netflix and HSBC, illustrate the practical implementations and trade-offs of operating in a multi-cloud environment. The paper concludes by identifying emerging trends such as AI-driven DataOps, decentralized data mesh architectures, and serverless ETL models, which are poised to redefine the future of data engineering.
Ultimately, this research serves as both a diagnostic and a prescriptive guide for engineers, architects, and data strategists seeking to navigate the complex terrain of multi-cloud data ecosystems with efficiency, compliance, and innovation.
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Copyright (c) 2022 Kishore Arul
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