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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

Facilitating data sharing with Delta

JDBC/ODBC connections or HTTP connections via REST APIs are good for sharing modest data but may become a bottleneck for larger datasets. Consider the scenario of sharing curated data with external vendors or partners. There are some firms whose business model is centered around data sharing, such as S&P, Bloomberg, FactSet, Nasdaq, and SafeGraph. They aim to be the source of truth for financial datasets, which every other financial institution will be interested in consuming for downstream analysis and to augment their own datasets. Wouldn't it be nice not to have to copy the data multiple times?

It is best to use cloud storage access directly to avoid unnecessary platform-related bottlenecks. That is what Delta sharing attempts to do – provide an open standard to securely and seamlessly share large volumes of data in Parquet/Delta with a wide variety of consumers and an easy way to govern and audit. Consumers can be from pandas...

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