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 introduction to Amazon Athena

Amazon Athena was originally launched as a service that simply provided a way to run SQL queries against data in an S3-based data lake. However, over the years, AWS had added a lot of additional functionality to Athena, enabling features like running queries against other databases (not just S3-based data), and supporting the use of Spark based Notebooks for querying data (in addition to SQL queries).

Structured Query Language (SQL) was invented at IBM in the 1970s but has remained an extremely popular language for querying data throughout the decades. Every day, millions of people across the world use SQL directly to explore data in a variety of databases, and many more use applications (whether business applications, mobile applications, or others) that, under the covers, use SQL to query a database.

Facebook, the social media network, has very large datasets and complex data analysis requirements and found that existing tools in the Hadoop...