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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

Architecting the Electroniz data lake

In the previous chapter, Chapter 4, Understanding Data Pipelines, we introduced the sample lakehouse project for a leading big-box store named Electroniz that sells electronic goods. We are going to assume that the final contract has been awarded, so the next step is to start building the data lakehouse. Now, it's time to put the skills that we learned in Chapter 4, Understanding Data Pipelines, to good use. Since our company is big on following best practices, the data engineering team has decided to diligently follow all of the steps in the Process of creating a data pipeline section.

As efficient data engineers, we will kick-start the process by conducting discovery sessions with customer groups. Often, conducting discovery sessions and extracting useful information from a customer can be very challenging. At times, you should expect to encounter varying personalities and pushbacks since everyone might not be on board with the global...

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