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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

Schema evolution

Schema evolution can be described as a technique that's used to adapt to ongoing structural changes to data. As systems mature and add more functionality, schema evolution is inevitable. Therefore, adapting to schema evolution is an extremely important requirement of modern-day pipelines.

It is customary to start developing pipelines so that they have base schemas for tables at the start of the project. Frequently, by the time things move into production, there is a very high likelihood that the schema for some incoming file or table has changed. But why is this such a big problem?

Important

A data engineer should never make the mistake of assuming that the schema of incoming data will never change. Instead, prepare the pipelines so that they auto-adjust to this evolution.

Let's discuss an example scenario to illustrate this point. Let's assume your pipelines have been deployed in production and that, for a while, you have been ingesting...

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