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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

Getting started with SQL query engines

In the world of big data, the way we store and analyze data has undergone a significant transformation. Traditional data warehouses, which were once the go-to solution for data analysis, have given way to more modern and scalable approaches, such as SQL query engines. These engines, such as Trino (formerly known as Presto), Dremio, and Apache Spark SQL, offer a high-performance, cost-effective, and flexible alternative to traditional data warehouses.

Next, we are going to see the main differences between data warehouses and SQL query engines.

The limitations of traditional data warehouses

Traditional data warehouses were designed to store and analyze structured data from relational databases. However, with the advent of big data and the proliferation of diverse data sources, such as log files, sensor data, and social media data, the limitations of data warehouses became apparent. These limitations include the following:

  • Scalability...
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