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

Architecting and Implementing Data Lakes and Data Lake Houses

In this section of the book, we examine an approach for architecting a high-level data pipeline and then dive into the specifics of data ingestion and transformation. We will examine different types of data consumers, learn about the important role of data marts and data warehouses, and finally put it all together by orchestrating our own data pipelines. We get hands-on with various AWS services for data ingestion (Amazon Kinesis and DMS), transformation (AWS Glue Studio), consumption (AWS Glue DataBrew), and pipeline orchestration (Step Functions).

This section comprises the following chapters:

Chapter 5, Architecting Data Engineering Pipelines
Chapter 6, Ingesting Batch and Streaming Data
Chapter 7, Transforming Data to Optimize for Analytics
Chapter 8, Identifying and Enabling Data Consumers
Chapter 9, A Deeper Dive into Data Marts and Amazon Redshift
Chapter 10, Orchestrating...

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You're reading from Data Engineering with AWS Acquire the skills to design and build AWS-based data transformation pipelines like a pro

Table of Contents (24) Chapters

Architecting and Implementing Data Lakes and Data Lake Houses

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Table of Contents (24) Chapters

Architecting and Implementing Data Lakes and Data Lake Houses

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