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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 Create scalable pipelines that ingest, curate, and aggregate complex data in a timely and secure way

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077743
Length 480 pages
Edition 1st Edition
Languages
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Author (1):
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Manoj Kukreja Manoj Kukreja
Author Profile Icon Manoj Kukreja
Manoj Kukreja
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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 FREE CHAPTER 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

The deployment strategy

We have worked extremely hard to get to the point where we have three functional pipelines, as follows:

  • The Electroniz ingestion pipeline: electroniz_batch_ingestion_pipeline
  • The Electroniz curation pipeline: electroniz_curation_pipeline
  • The Electroniz aggregation pipeline: electroniz_aggregation_pipeline

Just as a recap, in the last few chapters, we followed multiple steps in order to create these pipelines. After their creation, we invoked each one manually to unit test their functionality. Finally, we validated the data that each one produced to make sure it matched the expectation of the Electroniz use cases. That's a lot of work, so we should be proud to have reached this far.

Assuming we are happy with the outcomes of the unit tests performed on the preceding pipelines, it is time to start thinking about the best way to deploy these pipelines in production. As per best practices, the three pipelines should run as one complete...

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