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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

Deploying a local cluster using Kind

Deploying a local Kubernetes cluster can be extremely helpful for learning, testing, and preparing deployments for a production environment as Kubernetes is the same, wherever you run it. Let’s start by deploying a single-node local cluster using Kind.

Installing kind

Kind is a tool that allows you to run Kubernetes on your local machine inside Docker containers. Besides being light and easy to set up, Kind delivers performance with the same Kubernetes standards. Kind clusters pass upstream Kubernetes conformance testing.

Kind is distributed as a single binary file. You can install it easily with package managers (make sure you have Docker already installed):

  • If you’re using macOS, use Homebrew to install kind, like so:
    brew install kind
  • If you’re using a Linux distribution (Ubuntu, for instance), you can install it with curl:
    $ [ $(uname -m) = x86_64 ] && curl -Lo ./kind https://kind.sigs.k8s.io/dl...
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