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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

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781801070492

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

MLflow

Concepts

Data Science

Author (1):

Brian Lipp

View More author details

Table of Contents (19) Chapters

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

Why subscribe?

18. Other Books You May Enjoy

Summary

We have covered a great deal of information in this chapter. To summarize, we have looked at loading incremental data efficiently, delved deeper into the Databricks Workflows API, dabbled with the REST API, and also worked with AWS Lambda.

In this next chapter, we’ll go full steam ahead with data governance!

The rest of the chapter is locked

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Authors (1)

Brian Lipp

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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