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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

Data migration

Technologies are constantly evolving. It is important to choose a platform and architecture that is future-proof and extensible and supports a pluggable paradigm to play nicely with other tools of an ecosystem. So gravitating towards open data formats, open source tooling, and cloud-based architecture with separation of compute and storage, you can dodge the main bullets. There will be a time when this is no longer sustainable and the whole data platform needs a refreshing overhaul. Some examples of this that we’ve seen in recent years is migration from Hadoop-based systems that are complex and difficult to manage to cloud-native data platforms. The same is true of expensive data warehousing solutions such as Netezza, Teradata, and Exadata. Migration projects are expensive, time-consuming, and critical to the overall value of a business and tech investments and need to be planned and executed very carefully.

How will you determine whether to patch an existing...

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