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Big Data on Kubernetes

You're reading from   Big Data on Kubernetes A practical guide to building efficient and scalable data solutions

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835462140
Length 296 pages
Edition 1st Edition
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Author (1):
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Neylson Crepalde Neylson Crepalde
Author Profile Icon Neylson Crepalde
Neylson Crepalde
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1:Docker and Kubernetes FREE CHAPTER
2. Chapter 1: Getting Started with Containers 3. Chapter 2: Kubernetes Architecture 4. Chapter 3: Getting Hands-On with Kubernetes 5. Part 2: Big Data Stack
6. Chapter 4: The Modern Data Stack 7. Chapter 5: Big Data Processing with Apache Spark 8. Chapter 6: Building Pipelines with Apache Airflow 9. Chapter 7: Apache Kafka for Real-Time Events and Data Ingestion 10. Part 3: Connecting It All Together
11. Chapter 8: Deploying the Big Data Stack on Kubernetes 12. Chapter 9: Data Consumption Layer 13. Chapter 10: Building a Big Data Pipeline on Kubernetes 14. Chapter 11: Generative AI on Kubernetes 15. Chapter 12: Where to Go from Here 16. Index 17. Other Books You May Enjoy

Summary

In this chapter, we explored the next steps in your journey toward mastering big data on Kubernetes. We covered several important topics that are essential for building a robust and production-ready big data infrastructure on Kubernetes, including Kubernetes monitoring and application monitoring, building a service mesh, important security considerations to take into account in a production environment, scalability automation methods, GitOps and CI/CD for Kubernetes, and Kubernetes cost control.

We also discussed the importance of having a team with the right skills and expertise to tackle these challenges successfully. We covered the key roles and skill sets required for each of the topics mentioned, including SREs, DevOps engineers, cloud engineers, security engineers, and release engineers.

Congratulations on reaching the end of this book! You’ve taken a significant step toward mastering the art of running big data workloads on Kubernetes. The journey ahead...

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