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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

Summary

In this chapter, we learned about the IaaS, PaaS, and SaaS services in Azure that can help a data engineer build a data lake. We also discussed that cloud vendors provide many different options to perform similar operations. It is up to the data engineer to choose the right service that provides the customer with benefits based on their usage patterns, in-house skills, and budget.

The modern-day data pipeline requires careful planning, design, development, and deployment. In the next chapter, we will learn about the life cycle of a data pipeline and effective strategies for each phase of data pipeline creation.

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