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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

The Modern Data Stack

In this chapter, we will explore the modern data architecture that has emerged for building scalable and flexible data platforms. Specifically, we will cover the Lambda architecture pattern and how it enables real-time data processing along with batch data analytics. You will learn about the key components of the Lambda architecture, including the batch processing layer for historical data, the speed processing layer for real-time data, and the serving layer for unified queries. We will discuss how technologies such as Apache Spark, Apache Kafka, and Apache Airflow can be used to implement these layers at scale.

By the end of the chapter, you will understand the core design principles and technology choices for building a modern data lake. You will be able to explain the benefits of the Lambda architecture over traditional data warehouse designs. Most importantly, you will have the conceptual foundation to start architecting your own modern data platform.

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