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Modern Data Architectures with Python

You're reading from Modern Data Architectures with Python

Product type Book

Published in Sep 2023

Publisher Packt

ISBN-13 9781801070492

Pages 318 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Brian Lipp

Table of Contents (19) Chapters

Preface

1. Part 1:Fundamental Data Knowledge

2. Chapter 1: Modern Data Processing Architecture

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

Why subscribe?

18. Other Books You May Enjoy

Summary

So, here we are at the end of our project and data platform architecture journey. We covered topics including Spark, Delta Lake, Lambda architecture, Kafka, and MLOps. We also looked at how to build pipelines, package those pipelines, and deploy them centrally. Also, we delved into governance and platform design. This chapter and Chapter 11 built the initial beginnings of production-ready data pipelines. These pipelines can be used as the foundation of your data platform.

It’s my hope that you can take this project as a starting point for your next data platform project and build off it. I have presented many ideas and kernels of best practices. It’s up to you to continue the journey in your own projects. It doesn’t matter whether you choose the same tools or replace them with other tools that you prefer, the fundamental ideas should stay the same.

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Authors (1)

Brian Lipp

Brian Lipp is a Technology Polyglot, Engineer, and Solution Architect with a wide skillset in many technology domains. His programming background has ranged from R, Python, and Scala, to Go and Rust development. He has worked on Big Data systems, Data Lakes, data warehouses, and backend software engineering. Brian earned a Master of Science, CSIS from Pace University in 2009. He is currently a Sr. Data Engineer working with large Tech firms to build Data Ecosystems.

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