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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

Table of Contents (17) Chapters

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

Chapter 2: Discovering Storage and Compute Data Lakes

In the previous chapter, we discussed the immense power that data possesses, but with immense power comes increased responsibility. In the past, the key focus of organizations has been to detect trends with data, with the goal of revenue acceleration. Very commonly, however, they have paid less attention to vulnerabilities caused by inconsistent data management and delivery.

In this chapter, we will discuss some ways a data lake can effectively deal with the ever-growing demands of the analytical world.

In this chapter, we will cover the following topics:

  • Introducing data lakes
  • Segregating storage and compute in a data lake
  • Discovering data lake architectures
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