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The Hybrid Data Platform: Architecting Cloud-Native Pipelines with AWS Glue and Kubernetes as a Data Engineer
Paperback
Series: Cloud Data Engineering Series: Pipelines, Automation, and Modern Architectures
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Publisher: Independently Published
Published: Sep 29 2025
Pages: 114
Weight: 0.47
Height: 0.24 Width: 7.00 Depth: 10.00
Language: English
The Hybrid Data Platform: Architecting Cloud-Native Pipelines with AWS Glue and Kubernetes as a Data Engineer
Building modern data pipelines isn't just about moving information from point A to point B-it's about creating systems that are scalable, governed, and adaptable to the demands of today's enterprises. In a world where data volume, velocity, and variety keep increasing, how do you design architectures that are both efficient and future-ready?
This book answers that question by showing how AWS Glue and Kubernetes can be combined to create hybrid data platforms that balance automation with flexibility. Written for data engineers, architects, and technical leaders, it provides a step-by-step framework for architecting cloud-native pipelines that handle batch, streaming, and advanced analytics workloads. Whether you're aiming to improve enterprise reporting, enable machine learning pipelines, or expand into multi-cloud operations, this book gives you the strategies and tools to succeed.
What sets this book apart is its practical structure, moving from foundations to real-world implementations. You'll explore:
The Evolution of Data Engineering - why hybrid architectures are becoming essential.
Foundations of AWS Glue and Kubernetes - core components, architecture, and how they complement each other.
Designing Hybrid Pipelines - patterns that integrate serverless workflows with containerized workloads.
Building Data Lakes and Handling Mixed Workloads - strategies for batch, streaming, and real-time data.
Security, Governance, and Compliance - IAM, RBAC, and regulatory alignment in hybrid environments.
Monitoring, Logging, and Optimization - how to ensure reliability, observability, and cost efficiency.
Real-World Use Cases and Future-Proofing - enterprise analytics, AI integration, and sustainable scaling.
Every chapter blends technical depth with practical insights, supported by extended code snippets, deployment templates, and curated recommendations in the appendix. The result is a resource that doesn't just explain hybrid data platforms-it equips you to build and operate them confidently.
If you're a data engineer looking to stay ahead of the curve, or a decision-maker seeking to guide your team toward sustainable cloud-native solutions, this book will show you how to architect hybrid data platforms that scale with your needs.
Take the next step toward mastering hybrid cloud data engineering-make this book your guide to building pipelines that deliver both immediate results and long-term value.
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