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Data Engineering with AWS

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

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781804614426
Length 636 pages
Edition 2nd Edition
Tools
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Author (1):
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Gareth Eagar Gareth Eagar
Author Profile Icon Gareth Eagar
Gareth Eagar
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Table of Contents (24) Chapters Close

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

Hands-on – reviewing reviews with Amazon Comprehend

Imagine that you work for a large hotel chain and have been tasked with developing a process for identifying negative reviews that have been posted on your website. This will help the customer service teams follow up with the customer.

If your company was getting hundreds of reviews every day, it would be time-consuming to have someone read the entire review every time a new review was posted. Luckily, you have recently heard about Amazon Comprehend, so you decide to develop a small Proof of Concept (PoC) test to see whether Amazon Comprehend can help.

If your PoC is successful, you will want to have a decoupled process that receives reviews once they have been posted, calls Amazon Comprehend to determine the sentiment of the review, and then takes a follow-up action if the review is negative or mixed. Therefore, you decide to build your PoC in the same way, using Amazon Simple Queue Service (SQS) to receive reviews...

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