You're reading from Data Engineering with AWS Acquire the skills to design and build AWS-based data transformation pipelines like a pro

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Length 636 pages

Edition 2nd Edition

Tools

AWS

Concepts

Data Engineering

Author (1):

Gareth Eagar

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Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering FREE CHAPTER

3. Data Management Architectures for Analytics

4. The AWS Data Engineer’s Toolkit

5. Data Governance, Security, and Cataloging

6. Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations

7. Architecting Data Engineering Pipelines

8. Ingesting Batch and Streaming Data

9. Transforming Data to Optimize for Analytics

10. Identifying and Enabling Data Consumers

11. A Deeper Dive into Data Marts and Amazon Redshift

12. Orchestrating the Data Pipeline

13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

14. Ad Hoc Queries with Amazon Athena

15. Visualizing Data with Amazon QuickSight

16. Enabling Artificial Intelligence and Machine Learning

17. Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World

18. Building Transactional Data Lakes

19. Implementing a Data Mesh Strategy

20. Building a Modern Data Platform on AWS

21. Wrapping Up the First Part of Your Learning Journey

22. Other Books You May Enjoy

23. Index

An overview of Delta Lake, Apache Hudi, and Apache Iceberg

The three table formats that we are reviewing in this book all provide similar functionality, as outlined above, but they also all have their own unique features and slightly different implementations. In this section, we are going to do a deep dive into each of the three open table formats.

Deep dive into Delta Lake

Let’s start by looking at Delta Lake; however, we will not be covering the enhanced capabilities available as part of the paid Databricks offering. For example, Delta Live Tables provides ETL pipeline functionality, but is not open-sourced, so is not covered here.

Delta Lake has become a very popular table format, in large part as a result of Databricks having a very popular Lakehouse offering that incorporates Delta Lake. Databricks has made all Delta Lake API’s open-source, including a number of performance optimization features that they initially built for their paying customers...