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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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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

Faking data with Mockaroo

Faking data is a very important topic for anyone working with data pipelines. You are not always able to use real data. This could be for many reasons, including legal and company policies. In fact, this has often been the case for me. Faking data can also be problematic because does the fake data fully resemble your real data? Probably not. I think for this purpose we are mimicking real data, so it’s perfectly fine.

Mockaroo is a free hosted data service that can also be used to create simple REST APIs. Here, I created our three raw data schemas and then clicked on the button that says CREATE API.

Here, we are creating a schema for our machine API. We are using two Number and one Binomial Distribution column types. When done, you can click CREATE API.

Figure 12.9: Mockaroo machine schema

Figure 12.9: Mockaroo machine schema

Next, we are creating a schema for our sales API. We are using several columns, as outlined in the following figure. When done...

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