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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 5: Data Consolidation in Delta Lake

"Faith makes you stable and steady. It brings out the totality in you.

Consolidation of your energy is faith. Dissemination of energy is doubt."

– Sri Sri Ravi Shankar, on Transcendental Meditation

In the previous chapters, we discussed the quality of Delta and why it has become the first choice in big data processing. In this chapter, we will focus on how to consolidate disparate datasets into one or more data lakes backed by Delta so that you can build all kinds of use cases on a single source of truth without having to move data or stitch together multiple systems. We have already looked into the special features that Delta offers, including ACID transaction support, schema evolution, time travel, fine-grained data operations, and also big data design blueprints (such as the medallion architecture) in the context of data workflows. In this chapter, we will use those...

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