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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

The DataFrame API and the Spark SQL API

Spark provides different APIs built on top of the core RDD API (the native, low-level Spark language) to make it easier to develop distributed data processing applications. The two most popular higher-level APIs are the DataFrame API and the Spark SQL API.

The DataFrames API provides a domain-specific language to manipulate distributed datasets organized into named columns. Conceptually, it is equivalent to a table in a relational database or a DataFrame in Python pandas, but with richer optimizations under the hood. The DataFrames API enables users to abstract data processing operations behind domain-specific terminology such as grouping and joining instead of thinking in map and reduce operations.

The Spark SQL API builds further on top of the DataFrames API by exposing Spark SQL, a Spark module for structured data processing. Spark SQL allows users to run SQL queries against DataFrames to filter or aggregate data. The SQL queries get...

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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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