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Simplifying Data Engineering and Analytics with Delta

You're reading from   Simplifying Data Engineering and Analytics with Delta Create analytics-ready data that fuels artificial intelligence and business intelligence

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781801814867
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Anindita Mahapatra Anindita Mahapatra
Author Profile Icon Anindita Mahapatra
Anindita Mahapatra
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Table of Contents (18) Chapters Close

Preface 1. Section 1 – Introduction to Delta Lake and Data Engineering Principles
2. Chapter 1: Introduction to Data Engineering FREE CHAPTER 3. Chapter 2: Data Modeling and ETL 4. Chapter 3: Delta – The Foundation Block for Big Data 5. Section 2 – End-to-End Process of Building Delta Pipelines
6. Chapter 4: Unifying Batch and Streaming with Delta 7. Chapter 5: Data Consolidation in Delta Lake 8. Chapter 6: Solving Common Data Pattern Scenarios with Delta 9. Chapter 7: Delta for Data Warehouse Use Cases 10. Chapter 8: Handling Atypical Data Scenarios with Delta 11. Chapter 9: Delta for Reproducible Machine Learning Pipelines 12. Chapter 10: Delta for Data Products and Services 13. Section 3 – Operationalizing and Productionalizing Delta Pipelines
14. Chapter 11: Operationalizing Data and ML Pipelines 15. Chapter 12: Optimizing Cost and Performance with Delta 16. Chapter 13: Managing Your Data Journey 17. Other Books You May Enjoy

Chapter 4: Unifying Batch and Streaming with Delta

"We are only as strong as we are united, as weak as we are divided".

– J.K. Rowling, author of the Harry Potter series

In the last chapter, we examined Delta's capabilities and how it solves the challenges of traditional data lakes to give you curated data that is the foundation for sound insights without having to solve common operational problems over and over again. In this chapter, we will look at the two patterns of ingestion in data systems, namely, batch and streaming. Traditionally, they would have required two separate pipelines and the associated cost and effort to create, maintain, and reconcile data between the two pipelines. Thanks to protocols such as Delta, these two pipelines can now be consolidated.

In particular, we will be covering the following topics:

  • Moving toward real-time systems
  • Streaming ETL
  • Handling streaming scenarios...
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