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

Building Transactional Data Lakes

In the last few years, new technologies have emerged that have significantly enhanced the capabilities of traditional data lakes, enabling them to operate similarly to a data warehouse. These new technologies provide all the benefits of data lakes (such as low-cost object storage, and the ability to use serverless data processing services) while also making it much easier to update data in the data lake (amongst other benefits).

Traditional data lakes were built on the Apache Hive technology stack, which enables you to store data in various file formats (such as CSV, JSON, Parquet, and Avro). Hive enabled many tens of thousands of data lakes to be built on object storage, but over the years the limitations of Hive became more clear, as we will discuss in this chapter.

To overcome these limitations, a number of new table formats have been created by a number of different companies and open-source organizations. Keep reading to learn more...