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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

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835462140

Length 296 pages

Edition 1st Edition

Languages

Python

Tools

Kubernetes

Concepts

Big Data

Author (1):

Neylson Crepalde

View More author details

Table of Contents (18) Chapters

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

Why subscribe?

17. Other Books You May Enjoy

Summary

In this chapter, you learned how to deploy and manage key big data technologies such as Apache Spark, Apache Airflow, and Apache Kafka on Kubernetes. Deploying these tools on Kubernetes provides several benefits, including simplified operations, better resource utilization, scaling, high availability, and unified cluster management.

You started by deploying the Spark operator on Kubernetes and running a Spark application to process data from Amazon S3. This allows you to leverage Kubernetes for running Spark jobs in a cloud-native way, taking advantage of dynamic resource allocation and scaling.

Next, you deployed Apache Airflow on Kubernetes using the official Helm chart. You configured Airflow to run with the Kubernetes executor, enabling it to dynamically launch tasks on Kubernetes. You also set up remote logging to Amazon S3 for easier monitoring and debugging. Running Airflow on Kubernetes improves reliability, scalability, and resource utilization for orchestrating...

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Authors (1)

Neylson Crepalde

Neylson Crepalde

Neylson Crepalde is a Generative AI Strategist at AWS. Prior to that, he was CTO at A3Data, a consulting company focused on Data, Analytics and Artificial Intelligence. He holds a PhD in Economic Sociology, a master in Sociology of Culture, an MBA in Cultural Management and a Bachelor in Orchestra Conducting. He has been working with data since 2015. He is committed to sharing knowledge with people of every professional level and helping data teams achieve their best. He is several times AWS certified, Spark certified, Neo4j certified and Airflow certified. Neylson has been teaching for 10+ years now in colleges and MBA programs and he gives regular talks and lectures on Data Architecture, AI strategy, Data Governance and Network Science.

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