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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

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 18. Other Books You May Enjoy

Documentation

When starting out on a project, it’s good to catch up on the basics of what the project is about and how it will be interacted with. Here, we will lay out our schemas and high-level C4 System Context diagrams. For these diagrams, I used PlantUML code, which is another simple language for creating diagrams. PyCharm will display them and check your syntax so it is very easy to work with.

Schema diagram

Schema diagrams are very useful for users who want to get a basic understanding of the data and how they might use it. Normally, in a schema diagram, you will find the field names, the types, and sometimes sample data. This type of diagram works well for structured data with few columns. If your data is semi-structured or has a significant number of columns, I would avoid using this diagram and use something in JSON format instead.

Here we have 3 tables in our Bronze layer: sales, machine_raw, and sap_BSEG.

Figure 12.1: Bronze layer 1

Figure 12.1: Bronze layer...

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