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Data Engineering with AWS

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

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781804614426
Length 636 pages
Edition 2nd Edition
Tools
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Author (1):
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Gareth Eagar Gareth Eagar
Author Profile Icon Gareth Eagar
Gareth Eagar
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Toc

Table of Contents (24) Chapters Close

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...

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