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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
Author (1):
Brian Lipp Brian Lipp
Profile icon Brian Lipp
Toc

Table of Contents (19) Chapters close

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 18. Other Books You May Enjoy

Building out the Groundwork

In this chapter, we will set up our environment and build the template for all the hard work we will do in the final chapter on our project. Some of the tooling we will use might be new and we will introduce it with explanations. The main tooling introduction is to GitHub Actions, which is a CI tool used to automate code-related tasks. We will be using this to run code checks in all of our repos. Poetry will be used to manage our Python code and package it into a PyPI repo. Organizing our code like this helps in many ways and allows us to share the code across systems. Lastly, we will be working with the PyPi public system to deploy and manage our Python packages. This isn’t the normal process, but to avoid creating a private server, this public service was used. In production, typically, you normally use a hosted PyPI service or hosts your own server. Those tools were chosen simply to introduce something new in the final project. As mentioned before...

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