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Modern Data Architecture on AWS

You're reading from Modern Data Architecture on AWS

Product type Book

Published in Aug 2023

Publisher Packt

ISBN-13 9781801813396

Pages 420 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Behram Irani

Table of Contents (24) Chapters

Preface

1. Part 1: Foundational Data Lake

2. Prologue: The Data and Analytics Journey So Far

3. Chapter 1: Modern Data Architecture on AWS

4. Chapter 2: Scalable Data Lakes

5. Part 2: Purpose-Built Services And Unified Data Access

6. Chapter 3: Batch Data Ingestion

7. Chapter 4: Streaming Data Ingestion

8. Chapter 5: Data Processing

9. Chapter 6: Interactive Analytics

10. Chapter 7: Data Warehousing

11. Chapter 8: Data Sharing

12. Chapter 9: Data Federation

13. Chapter 10: Predictive Analytics

14. Chapter 11: Generative AI

15. Chapter 12: Operational Analytics

16. Chapter 13: Business Intelligence

17. Part 3: Govern, Scale, Optimize And Operationalize

18. Chapter 14: Data Governance

19. Chapter 15: Data Mesh

20. Chapter 16: Performant and Cost-Effective Data Platform

21. Chapter 17: Automate, Operationalize, and Monetize

22. Index

Why subscribe?

23. Other Books You May Enjoy

Data catalogs

We talked about a data lake in AWS being a combination of the data in S3 buckets and the metadata of this data stored in a catalog. We will solve the mystery of creating a technical catalog in AWS by introducing another critical service for building a modern data platform, AWS Glue—a serverless data integration service. Now, Glue is actually an umbrella service consisting of multiple parts. It has the Glue ETL part, which is used for building data integration work, and we have multiple chapters on data ingestion and integration. The component of Glue that is relevant to our data catalog discussion is Glue Data Catalog. Let’s unfold more about the catalog in Glue and how it helps with our data lake in S3.

Glue Data Catalog

As the data passes through layers of the data lake in S3, the metadata of the data is captured and stored in Glue Data Catalog. It creates and stores the technical metadata in the form of data definition language (DDL) statements...

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Authors (1)

Behram Irani

Behram Irani is currently a technology leader with Amazon Web Services (AWS) specializing in data, analytics and AI/ML. He has spent over 18 years in the tech industry helping organizations, from start-ups to large-scale enterprises, modernize their data platforms. In the last 6 years working at AWS, Behram has been a thought leader in the data, analytics and AI/ML space; publishing multiple papers and leading the digital transformation efforts for many organizations across the globe. Behram has completed his Bachelor of Engineering in Computer Science from the University of Pune and has an MBA degree from the University of Florida.

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