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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

Chapter 1: Introduction to Data Engineering

"Water, water, everywhere, nor any drop to drink...

Data data everywhere, not a drop of insight!"

With the vast exodus of data around us, it is important to crunch it meaningfully and promptly to extract value from all the noise. This is where data engineering steps in. If collecting data is the first step, drawing useful insights is the next. Data engineering encompasses several personas that come together with their unique individual skill sets and processes to bring this to fruition. Data usually outlives the technology, and it continues to grow. New tools and frameworks come to the forefront to solve a lot of old problems. It is important to understand business requirements, the accompanying tech challenges, and typical shifts in paradigms to solve these age-old problems in a better manner.

By the end of this chapter, you should have an appreciation of the data landscape, the players, and the advances in distributed computing and cloud infrastructure that make it possible to support the high pace of innovation.

In this chapter, we will cover the following topics:

  • The motivation behind data engineering
  • Data personas
  • Big data ecosystem
  • Evolution of data stores
  • Trends in distributed computing
  • Business justification for tech spending
You have been reading a chapter from
Simplifying Data Engineering and Analytics with Delta
Published in: Jul 2022
Publisher: Packt
ISBN-13: 9781801814867
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