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

Moving data between a data lake and Redshift

Moving data between a data lake and a data warehouse, such as Amazon Redshift, is a common requirement for many use cases. Data may be cleansed and processed with Glue ETL jobs in the data lake, for example, and then hot data can be loaded into Redshift so that it can be queried via BI tools with optimal performance.

In the same way, there are certain use cases where data may be further processed in the data warehouse, and this newly processed data then needs to be exported back to the data lake so that other users and processes can consume this data.

In this section, we will examine some best practices and recommendations for both ingesting data from the data lake and exporting data back to the data lake.

Optimizing data ingestion in Redshift

While there are various ways that you can insert data into Redshift, the recommended way is to bulk ingest data using the Redshift COPY command. The COPY command enables optimized...