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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

Exploring data pipelines

In Chapter 1, The Story of Data Engineering and Analytics, we talked about the journey of data. We equated data engineering to a vehicle that makes the journey of data possible through sharp turns and roadblocks to ultimately reach its destination as securely and timely as possible. If data engineering is a vehicle, then a data pipeline is the engine that makes the journey possible. The engine is simply a collection of components, each performing a specialized operation. Ultimately, all the parts and components working together can maneuver the vehicle in the desired direction.

In simple terms, a data pipeline is an engine that can move data through various stages of collection, curation, and aggregation to reach its analytics destination. As with the various parts and components of an engine, the data pipeline uses a series of actions to complete its work. Each action performs a specialized task (once or repeatedly) to contribute toward the end goal.

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