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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

Handling streaming scenarios

In this section, we will see how to tackle common streaming requirements such as joining a stream with other data, some of which could be a mix of batch and streams; recovering from an intermittent failure scenario, which may involve restarting the stream after a period of inactivity; and handling late-arriving data, among other scenarios.

Joining with other static and dynamic datasets

Joins are a very common operation and streaming datasets are no exception. Very often, they need to be joined with other datasets, usually a slowly-changing dimension dataset to fortify the data for rich analytics. Let's consider an IoT use case where devices are being manufactured in lots and getting registered in a Delta lookup table. As the devices are deployed in the field, they start emitting sensor data that is streaming in nature and comes at a higher velocity. This IoT data needs to be joined with the device's lookup data. Storing device data in Delta...

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