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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

Product type Paperback

Published in Jul 2022

Publisher Packt

ISBN-13 9781801814867

Length 334 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Artificial Intelligence

Author (1):

Anindita Mahapatra

View More author details

Table of Contents (18) Chapters

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

Technical requirements

To follow along with this chapter, make sure you have the code and instructions as detailed on GitHub here:

https://github.com/PacktPublishing/Simplifying-Data-Engineering-and-Analytics-with-Delta/tree/main/Chapter12

https://github.com/delta-io/delta/issues/920 is the proposed roadmap for select edge feature migration from Databricks to open source Delta primarily for performance enhancements.

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Authors (1)

Anindita Mahapatra

Anindita Mahapatra

Anindita Mahapatra is a lead solutions architect at Databricks in the data and AI space helping clients across all industry verticals reap value from their data infrastructure investments. She teaches a data engineering and analytics course at Harvard University as part of their extension school program. She has extensive big data and Hadoop consulting experience from Think Big/Teradata, prior to which she was managing the development of algorithmic app discovery and promotion for both Nokia and Microsoft stores. She holds a master's degree in liberal arts and management from Harvard Extension School, a master's in computer science from Boston University, and a bachelor's in computer science from BITS Pilani, India.

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