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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

Infrastructure as code

Until recently, infrastructure deployment has been an extremely time-consuming and expensive process. In a typical organization, a team of system administrators would be responsible for deploying hardware and software on several environments using a series of manually executed steps. Due to its manual nature, this process has major deficiencies:

  • Unique settings per environment: Every individual environment can have unique settings. To meet these requirements, the manual steps need to be documented for each environment.
  • Time-consuming: The documentation and execution of manual steps are very time-consuming.
  • Inconsistent and error-prone: Following manual steps for infrastructure deployments can lead to inconsistencies and errors.

Infrastructure as Code (IaC) is a practice to deploy and manage infrastructure components using source code. Using IaC, the system administration teams (also known as DevOps) are no longer bound by manual practices...

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