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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

Is cost always inversely proportional to performance?

Typically, higher performance is associated with higher costs. Spark provides options for tunable performance and cost. At a high level, it is a given that if your end-to-end latency is stringent or low, then your cost will be higher.

But using Delta to unify all your workloads on a single platform brings efficiencies of scale through automation and standardization, leading to cost reductions by reducing the number of hops and processing steps, which translates to a reduction in compute power. Also, when your queries run faster on the same hardware, you pay for a shorter duration of your running cloud computing cost. So yes, it is possible to improve performance and still contain the cost. SLA requirements are not compromised. Instead, superior architecture options are available, such as the unification of batch and streaming workloads, handling both schema enforcement alongside schema evolution, and the ability to handle unstructured...

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