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Data Engineering with AWS

You're reading from   Data Engineering with AWS Learn how to design and build cloud-based data transformation pipelines using AWS

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800560413
Length 482 pages
Edition 1st Edition
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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 (19) Chapters Close

Preface 1. Section 1: AWS Data Engineering Concepts and Trends
2. Chapter 1: An Introduction to Data Engineering FREE CHAPTER 3. Chapter 2: Data Management Architectures for Analytics 4. Chapter 3: The AWS Data Engineer's Toolkit 5. Chapter 4: Data Cataloging, Security, and Governance 6. Section 2: Architecting and Implementing Data Lakes and Data Lake Houses
7. Chapter 5: Architecting Data Engineering Pipelines 8. Chapter 6: Ingesting Batch and Streaming Data 9. Chapter 7: Transforming Data to Optimize for Analytics 10. Chapter 8: Identifying and Enabling Data Consumers 11. Chapter 9: Loading Data into a Data Mart 12. Chapter 10: Orchestrating the Data Pipeline 13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
14. Chapter 11: Ad Hoc Queries with Amazon Athena 15. Chapter 12: Visualizing Data with Amazon QuickSight 16. Chapter 13: Enabling Artificial Intelligence and Machine Learning 17. Chapter 14: Wrapping Up the First Part of Your Learning Journey 18. Other Books You May Enjoy

Imagining the future – a look at emerging trends

Technology seems to progress at an increasing velocity. For decades, relational databases from vendors such as Oracle were the primary technology for managing all data. Today, there is a wide range of different database types that can be used, depending on the use case (such as graph databases for highly connected datasets, or NoSQL databases for low-latency reading and writing for very large tables).

It was also not all that long ago that Hadoop MapReduce was the state-of-the-art technology for processing very large datasets, but today, most new projects would choose Apache Spark over a MapReduce implementation. And even Apache Spark itself has progressed from its initial release, with Spark 3.0 being released in June 2020. We have also seen the introduction of Spark Streaming, Spark ML, and Spark GraphX for different use cases.

No one can tell for certain what the next big thing will be, but in this section, we will...

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