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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

Data Consumption Layer

In today’s data-driven world, organizations are dealing with an ever-increasing volume of data, and the ability to effectively consume and analyze this data is crucial for making informed business decisions. As we delve into the realm of big data on Kubernetes, we must address the critical component of the data consumption layer. This layer serves as the bridge between the vast repositories of data and the business analysts who need to extract valuable insights and make decisions that have an impact on the business.

In this chapter, we will explore two powerful tools that will enable you to unlock the potential of your Kubernetes-based data architecture: Trino and Elasticsearch. Trino, a distributed SQL query engine, will empower you to directly query your data lake, eliminating the need for a traditional data warehouse. You will learn how to deploy Trino on Kubernetes, monitor its performance, and execute SQL queries against your data stored in Amazon...

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