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

AWS services for ingesting data

The first step in building big data analytic solutions is to ingest data from a variety of sources into AWS. In this section, we introduce some of the core AWS services designed to help with this; however, this should not be considered a comprehensive review of every possible way to ingest data into AWS.Don't feel overwhelmed by the number of services we cover in this section! We will explore approaches for deciding on the right service for your specific use case in later chapters, but it is important to have a good understanding of the available tools upfront.

Overview of Amazon Database Migration Service (DMS)

One of the most common ingestion use cases is to sync data from a database system into an analytic pipeline, either landing the data in an Amazon S3-based data lake, or in a data warehousing system such as Amazon Redshift.AWS Database Migration Service (DMS) is a versatile tool that can be used to migrate an existing database system to a new...