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

View More author details

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

DataOps as an approach to building data platforms

DataOps is a term that has been around since at least 2015 and refers to an agile approach to building data platforms and data products that borrows from some of the concepts of DevOps. Where DevOps transformed the approach to how software is engineered, DataOps transforms the approach by which data products are built.

Much like it is difficult to give an exact definition or outline an exact approach for other concepts we have discussed (such as data lakes and data meshes), it is similarly difficult to tie down one clear-cut definition for what is meant by DataOps. The original author of the term may have had a clear definition of what they meant, but over time, the term may come to mean different things to different people, and the meaning of the term as a whole may evolve.

In this section, we will attempt to focus on some of the core concepts of DataOps, and specifically, how they apply to building a data platform and data...