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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

AWS services for transforming data

Once your data is ingested into an appropriate AWS service, such as Amazon S3, the next stage of the pipeline needs to transform the data to optimize it for analytics and to make it available to your data consumers.Some of the tools we discussed in the previous section for ingesting data into AWS can perform light transformations as part of the ingestion process. For example, Amazon DMS can write out data in Parquet format (a format optimized for analytics), as can Kinesis Firehose. However, heavier transformations are often required to fully optimize your data for a differing set of analytic tasks and diverse data consumers, and in this section, we will examine some of the core AWS services that can be used for this.

Overview of AWS Lambda for light transformations

AWS Lambda provides a serverless environment for executing code, and is one of AWS's most popular services. You can trigger your Lambda function to execute your code in multiple ways...

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