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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

Displaying our data with dashboards

There are many ways to create dashboards; more often than not, I find simple notebooks with useful metrics are the more common dashboards. Here, I created a SQL-only notebook, which looks at the Gold sales table and creates a scatter plot of the data. This could be scheduled to update at any cadence needed using the schedule option at the top left.

Figure 12.12: Databricks chart 1

Figure 12.12: Databricks chart 1

Next, we will run a SQL command to do a count of failed engine data and passed engine data. Simple queries such as these are very common in dashboard notebooks.

Figure 12.13: Databricks chart 2

Figure 12.13: Databricks chart 2

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