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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

The rise of big data as a corporate asset

You don't need to look too far or too hard these days to hear about how big data and data analytics are transforming organizations and having an impact on society as a whole. We hear about how companies such as TikTok analyze large quantities of data to make personalized recommendations about which clip to show a user next. Also, we know how Amazon recommends products a customer may be interested in based on their purchase history. We read headlines about how big data could revolutionize the healthcare industry, or how stock pickers turn to big data to find the next breakout stock performer when the markets are down.

The most valuable companies in the US today are companies that are masters of managing huge data assets effectively, with the top five most valuable companies in Q4 2021 being the following:

  • Microsoft
  • Apple
  • Alphabet (Google)
  • Amazon
  • Tesla

For a long time, it was companies that managed natural gas and oil resources, such as ExxonMobil, that were high on the list of the most valuable companies on the US stock exchange. Today, ExxonMobil will often not even make the list of the top 30 companies. It is no wonder that the number of job listings for people with skillsets related to big data is on the rise.

There is also no doubt that data, when harnessed correctly and optimized for maximum analytic value, can be a game-changer for an organization. At the same time, those companies that are unable to effectively utilize their data assets risk losing a competitive advantage to others that do have a comprehensive data strategy and effective analytic and machine learning programs.

Organizations today tend to be in one of the following three states:

  • They have an effective data analytics and machine learning program that differentiates them from their competitors.
  • They are conducting proof of concept projects to evaluate how analytic and machine learning programs can help them achieve a competitive advantage.
  • Their leaders are having sleepless nights worrying about how their competitors are using analytics and machine learning programs to achieve a competitive advantage over them.

No matter where an organization currently is in their data journey, if they have been in existence for a while, they have likely faced a number of common data-related challenges. Let's look at how organizations have typically handled the challenge of ever-growing datasets.

You have been reading a chapter from
Data Engineering with AWS
Published in: Dec 2021
Publisher: Packt
ISBN-13: 9781800560413
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