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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

Data documentation

Data can get complex and often documentation is non-existent. We will look into two areas that can be documented with code. Using code to document GUI tools allows for code to be stored and reviewed effectively like any other code written.

diagrams

The diagrams library is very useful for creating solution diagrams. A solution diagram is often a broad picture of the architecture and key components. It can be organized in a way that explains key interactions.

Here, we are creating a small example document using the diagrams package:

from diagrams import Cluster, Diagram
from diagrams.aws.analytics import Quicksight, EMR
with Diagram("Data Platform", show=False):
    with Cluster("Dev"):
        dashboards = Quicksight("Tableau")
        spark_clusters = [EMR("Notebook_cluster"), EMR("Jobs_cluster")]
 ...
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