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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

Integrating Continous Integration into Your Workflow

As we grow our projects, many data projects go from being a scattering of notebooks to a continuous integration (CI)-driven application. In this chapter, we will go through some of the tooling and concepts for stringing together your Python scripts and notebooks into a working data application. We will be using Jenkins for CI, GitHub for source control, workflows for orchestration, and Terraform for Infrastructure as Code (IaC). Those tools can be swapped out for your preferred tool without much effort.

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

  • Python wheels and creating a Python package
  • CI with Jenkins
  • Working with source control using GitHub
  • Creating Databricks jobs and controlling several jobs using workflows
  • Creating IaC using Terraform
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