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Data Engineering with Apache Spark, Delta Lake, and Lakehouse

You're reading from  Data Engineering with Apache Spark, Delta Lake, and Lakehouse

Product type Book
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077743
Pages 480 pages
Edition 1st Edition
Languages
Author (1):
Manoj Kukreja Manoj Kukreja
Profile icon Manoj Kukreja
Toc

Table of Contents (17) Chapters close

Preface 1. Section 1: Modern Data Engineering and Tools
2. Chapter 1: The Story of Data Engineering and Analytics 3. Chapter 2: Discovering Storage and Compute Data Lakes 4. Chapter 3: Data Engineering on Microsoft Azure 5. Section 2: Data Pipelines and Stages of Data Engineering
6. Chapter 4: Understanding Data Pipelines 7. Chapter 5: Data Collection Stage – The Bronze Layer 8. Chapter 6: Understanding Delta Lake 9. Chapter 7: Data Curation Stage – The Silver Layer 10. Chapter 8: Data Aggregation Stage – The Gold Layer 11. Section 3: Data Engineering Challenges and Effective Deployment Strategies
12. Chapter 9: Deploying and Monitoring Pipelines in Production 13. Chapter 10: Solving Data Engineering Challenges 14. Chapter 11: Infrastructure Provisioning 15. Chapter 12: Continuous Integration and Deployment (CI/CD) of Data Pipelines 16. Other Books You May Enjoy

Verifying curated data in the silver layer

In the previous section, we ran electroniz_curation_pipeline four times, each time with a different hourly folder. If everything worked correctly, we could safely infer that the silver layer of the Electroniz lakehouse is now functional.

As per the curation process, the last step is to verify the curated data. We will use another notebook that already contains the code that performs the validations. You simply need to run it step by step as follows:

  1. The verification code is available in curation_verification_notebook.ipynb.
  2. Import the curation_verification_notebook.ipynb notebook into Azure Databricks. The steps are very similar to what was done previously for the curation notebook (electroniz_curation_notebook.ipynb):
    • On the Databricks workspace, click on Workspace. Then, click on Users.
    • Click on the arrow beside your username and click on Import.
    • Choose URL.
    • Use the following URL: https://github.com/PacktPublishing/Data-Engineering...
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