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Modern Data Architectures with Python

You're reading from   Modern Data Architectures with Python A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781801070492
Length 318 pages
Edition 1st Edition
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Author (1):
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Brian Lipp Brian Lipp
Author Profile Icon Brian Lipp
Brian Lipp
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Fundamental Data Knowledge
2. Chapter 1: Modern Data Processing Architecture FREE CHAPTER 3. Chapter 2: Understanding Data Analytics 4. Part 2: Data Engineering Toolset
5. Chapter 3: Apache Spark Deep Dive 6. Chapter 4: Batch and Stream Data Processing Using PySpark 7. Chapter 5: Streaming Data with Kafka 8. Part 3:Modernizing the Data Platform
9. Chapter 6: MLOps 10. Chapter 7: Data and Information Visualization 11. Chapter 8: Integrating Continous Integration into Your Workflow 12. Chapter 9: Orchestrating Your Data Workflows 13. Part 4:Hands-on Project
14. Chapter 10: Data Governance 15. Chapter 11: Building out the Groundwork 16. Chapter 12: Completing Our Project 17. Index 18. Other Books You May Enjoy

Apache Spark Deep Dive

One of the most fundamental questions for an architect is how they should store their data and what methodology they should use. For example, should they use a relational database, or should they use object storage? This chapter attempts to explain which storage pattern is best for your scenario. Then, we will go through how to set up Delta Lake, a hybrid approach to data storage in an object store. In most cases, we will stick to the Python API, but in some cases, we will have to use SQL. Lastly, we will cover the most important Apache Spark theory you need to know to build a data platform effectively.

In this chapter, we’re going to cover the following main topics:

  • Understand how Spark manages its cluster
  • How Spark processes data
  • How cloud storage varies and what options are available
  • How to create and manage Delta Lake tables and databases
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