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Data Engineering with AWS - Second Edition

You're reading from  Data Engineering with AWS - Second Edition

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781804614426
Pages 636 pages
Edition 2nd Edition
Languages
Author (1):
Gareth Eagar Gareth Eagar
Profile icon Gareth Eagar

Table of Contents (24) Chapters

Preface 1. Section 1: AWS Data Engineering Concepts and Trends
2. An Introduction to Data Engineering 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

Challenges that a data mesh approach attempts to resolve

Traditional data lakes and approaches served many organizations well for a long time, but as with everything, there are always new developments and approaches that help drive improvements.

In the previous chapter, we looked at how new table formats (such as Apache Iceberg) introduced new functionality that improved querying and processing data in data lakes. In a similar way, the concepts and approaches introduced by a data mesh help solve some different challenges of traditional data lakes and how data teams are structured.

Let’s look at a few of the traditional challenges that a data mesh helps solve.

Bottlenecks with a centralized data team

While not the case for every data lake, it was common for large enterprises to create a centralized team that would ingest data from transactional systems across the organization and then perform ETL tasks on that data (cleaning the data, joining data from across...

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