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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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Toc

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

Summary

Information is dynamic and constantly evolving, which is why success in business is based on how we make use of this continuously changing data.

The modern data platform is built on business-centric value chains rather than IT-centric coding pipelines. The focus is to provide insights faster by turning event streams into analytics-ready data. Stream processing naturally fits with time series data and supports the detection of patterns over time. For some scenarios, streaming is a must, for example – sensor data, advertisement data, server security logs, and clickstream data. In some others, it is not, but every batch job can be regarded as a streaming job with a longer trigger interval and because the dial is configurable, it can be tweaked to make it more real time in line with business demands without having to rewrite the pipeline.

In this chapter, we focused on stream processing for ingesting, processing, and storing data. In the next chapter, we will look...

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