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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

Moving toward real-time systems

As their names suggest, batch is a form of periodic ingestion of data, whereas streaming is a process where data ingestion is either continuous or in micro-batches. There is no denying that the trend is toward the real-time ingestion, analysis, and consumption of data. This gives rise to the question of why is every pipeline not a streaming one?

There may be several producers of data for the same target table. Some may be fast-moving, while others could be slower. If the nature of your data is such that it comes once a month, then we certainly do not want to have compute running more frequently than once a month from a cost savings perspective. Hence, some folks may say that cases such as these force us to have batch ingestion. In this chapter, we will present an argument to justify that batch is actually a type of streaming workload and that all workloads can be expressed as a streaming pipeline. You may argue, 'Isn't streaming more complex...

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