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Data Engineering with AWS

You're reading from Data Engineering with AWS Learn how to design and build cloud-based data transformation pipelines using AWS

Product type Paperback

Published in Dec 2021

Publisher Packt

ISBN-13 9781800560413

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Data Analysis

Author (1):

Gareth Eagar

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. Chapter 1: An Introduction to Data Engineering FREE CHAPTER

3. Chapter 2: Data Management Architectures for Analytics

4. Chapter 3: The AWS Data Engineer's Toolkit

5. Chapter 4: Data Cataloging, Security, and Governance

6. Section 2: Architecting and Implementing Data Lakes and Data Lake Houses

7. Chapter 5: Architecting Data Engineering Pipelines

8. Chapter 6: Ingesting Batch and Streaming Data

9. Chapter 7: Transforming Data to Optimize for Analytics

10. Chapter 8: Identifying and Enabling Data Consumers

11. Chapter 9: Loading Data into a Data Mart

12. Chapter 10: Orchestrating the Data Pipeline

13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

14. Chapter 11: Ad Hoc Queries with Amazon Athena

15. Chapter 12: Visualizing Data with Amazon QuickSight

16. Chapter 13: Enabling Artificial Intelligence and Machine Learning

17. Chapter 14: Wrapping Up the First Part of Your Learning Journey

18. Other Books You May Enjoy

Summary

In this chapter, we reviewed several ways to ingest common data types into AWS. We reviewed how AWS DMS can be used to replicate a relational database to S3, and how Amazon Kinesis and Amazon MSK can be used to ingest streaming data.

In the hands-on section of this chapter, we used both the AWS DMS and Amazon Kinesis services to ingest data and then used AWS Glue to add the newly ingested data to the data catalog and query the data with Amazon Athena.

In the next chapter, Chapter 7, Transforming Data to Optimize for Analytics, we will review how we can transform the ingested data to optimize it for analytics, a core task for data engineers.

The rest of the chapter is locked

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Authors (1)

Eagar

Eagar

Gareth Eagar has over 25 years of experience in the IT industry, starting in South Africa, working in the United Kingdom for a while, and now based in the USA. Having worked at AWS since 2017, Gareth has broad experience with a variety of AWS services, and deep expertise around building data platforms on AWS. While Gareth currently works as a Solutions Architect, he has also worked in AWS Professional Services, helping architect and implement data platforms for global customers. Gareth frequently speaks on data related topics.

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