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Simplifying Data Engineering and Analytics with Delta

You're reading from Simplifying Data Engineering and Analytics with Delta

Product type Book

Published in Jul 2022

Publisher Packt

ISBN-13 9781801814867

Pages 334 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Anindita Mahapatra

Table of Contents (18) Chapters

Preface

1. Section 1 – Introduction to Delta Lake and Data Engineering Principles

2. Chapter 1: Introduction to Data Engineering

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

Delta unifies all types of data

In this section, we will give you some examples of how to ingest (read) different data types into a Spark DataFrame and save it all in the Delta format in the data lake using a common API, df.write.format. ("delta") and this curated data is the single source of truth for all BI and AI use cases, as shown in the following diagram:

Figure 5.3 – Consolidating all your data in a central Delta Lake

The three main data types are structured, semi-structured, and unstructured, and Spark native APIs (along with partner-aided connectors) can ingest data from a wide variety of data sources to create a curated view that all consumers can access, depending on their privilege levels. In some cases, different Lines Of Business (LOBs) may want to have their own mini data lake, and that is perfectly fine as it follows a hub-spoke model of a central data repository for common access and specialized ones with tighter guardrails...

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Authors (1)

Anindita Mahapatra

Anindita Mahapatra is a 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 Thinkbig/Teradata prior to which she was managing development of algorithmic app discovery and promotion for both Nokia and Microsoft AppStores. She holds a Masters degree in Liberal Arts and Management from Harvard Extension School, a Masters in Computer Science from Boston University and a Bachelors in Computer Science from BITS Pilani, India.

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