DataHub Engineering and Architecture Reference: The Complete Guide for Developers and Engineers
William Smith
Jul 2025 · HiTeX Press
Ebook
250
Pages
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About this ebook
"DataHub Engineering and Architecture Reference"
The "DataHub Engineering and Architecture Reference" is an authoritative guide for architects, engineers, and technical leaders seeking a comprehensive understanding of DataHub, the open-source metadata platform shaping the modern data landscape. Beginning with foundational concepts, this book explores the evolution of DataHub, positioning it among both open-source and commercial metadata management solutions. Through in-depth discussions of metadata modeling, data catalogs, and key architectural drivers, readers gain a deep appreciation of DataHub’s unique contributions to metadata ecosystems and the vibrant community driving its open standards.
The coverage extends to the architectural heart of DataHub, meticulously dissecting its distributed, service-oriented design, asynchronous event-driven patterns, and scalable deployment modalities. Practical engineering insights are offered across metadata modeling, custom extensions, ingestion frameworks, API surfaces, and integration strategies that support hybrid and extensible deployments. Readers are provided detailed guidance on implementing lineage, ownership, classification, and graph-enriched metadata structures, as well as robust strategies for cross-system federation and real-time data ingestion.
Rounding out the reference, the book delivers expert guidance in critical operational areas, including security and compliance, performance optimization, reliability engineering, and DevOps practices. It offers best practices for deploying, monitoring, and scaling DataHub, integrating security controls, orchestrating resilient ingestion pipelines, and supporting enterprise-grade governance and observability requirements. The volume concludes by exploring advanced architectures—such as data mesh, MLOps integration, and metadata-driven automation—and situates DataHub within a rapidly evolving vendor and community landscape, making this an indispensable resource for those shaping the future of data platforms.
The "DataHub Engineering and Architecture Reference" is an authoritative guide for architects, engineers, and technical leaders seeking a comprehensive understanding of DataHub, the open-source metadata platform shaping the modern data landscape. Beginning with foundational concepts, this book explores the evolution of DataHub, positioning it among both open-source and commercial metadata management solutions. Through in-depth discussions of metadata modeling, data catalogs, and key architectural drivers, readers gain a deep appreciation of DataHub’s unique contributions to metadata ecosystems and the vibrant community driving its open standards.
The coverage extends to the architectural heart of DataHub, meticulously dissecting its distributed, service-oriented design, asynchronous event-driven patterns, and scalable deployment modalities. Practical engineering insights are offered across metadata modeling, custom extensions, ingestion frameworks, API surfaces, and integration strategies that support hybrid and extensible deployments. Readers are provided detailed guidance on implementing lineage, ownership, classification, and graph-enriched metadata structures, as well as robust strategies for cross-system federation and real-time data ingestion.
Rounding out the reference, the book delivers expert guidance in critical operational areas, including security and compliance, performance optimization, reliability engineering, and DevOps practices. It offers best practices for deploying, monitoring, and scaling DataHub, integrating security controls, orchestrating resilient ingestion pipelines, and supporting enterprise-grade governance and observability requirements. The volume concludes by exploring advanced architectures—such as data mesh, MLOps integration, and metadata-driven automation—and situates DataHub within a rapidly evolving vendor and community landscape, making this an indispensable resource for those shaping the future of data platforms.
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