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

Ingesting data from a relational database

A common source of data for analytical projects is data that comes from a relational database system such as MySQL, PostgreSQL, SQL Server, or an Oracle database. Organizations often have multiple siloed databases, and they want to bring the data from these varied databases into a central location for analytics.

It is common for these projects to include ingesting historical data that already exists in the database, as well as syncing ongoing new and changed data from the database. There are a variety of tools that can be used to ingest from database sources, as we will discuss in this section.

AWS DMS

The primary AWS service for ingesting data from a database is AWS DMS, though there are other ways to ingest data from a database source. As a data engineer, you need to evaluate both the source and the target to determine which ingestion tool will be best suited.

AWS DMS is intended for doing either one-off ingestion of historical...