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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

Discovering data lake architectures

Before we start the journey of data lake creation, it is important to understand how we undertake a normal journey in real life. Before taking a journey—a vacation, for instance —we often start by doing some preparations, such as these:

  • Plan—Shortlist destinations that we want to visit during our trip.
  • Reserve—Book the best airlines and hotels that fall within our budget.
  • Visit—Hopefully, our preparations will make the trip both comfortable and secure.

Preparing for the journey of data lake creation is no different, as we can note here:

  • Plan—Choose the correct data lake architecture that meets the objectives of the organization's analytical needs (destination).
  • Reserve—Procure resources (computer and human) required to create a data lake within the financial restrictions (budget).
  • Create—Create the necessary infrastructure and data pipelines based...
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