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Data Engineering with Apache Spark, Delta Lake, and Lakehouse

You're reading from   Data Engineering with Apache Spark, Delta Lake, and Lakehouse Create scalable pipelines that ingest, curate, and aggregate complex data in a timely and secure way

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077743
Length 480 pages
Edition 1st Edition
Languages
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Author (1):
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Manoj Kukreja Manoj Kukreja
Author Profile Icon Manoj Kukreja
Manoj Kukreja
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Modern Data Engineering and Tools
2. Chapter 1: The Story of Data Engineering and Analytics FREE CHAPTER 3. Chapter 2: Discovering Storage and Compute Data Lakes 4. Chapter 3: Data Engineering on Microsoft Azure 5. Section 2: Data Pipelines and Stages of Data Engineering
6. Chapter 4: Understanding Data Pipelines 7. Chapter 5: Data Collection Stage – The Bronze Layer 8. Chapter 6: Understanding Delta Lake 9. Chapter 7: Data Curation Stage – The Silver Layer 10. Chapter 8: Data Aggregation Stage – The Gold Layer 11. Section 3: Data Engineering Challenges and Effective Deployment Strategies
12. Chapter 9: Deploying and Monitoring Pipelines in Production 13. Chapter 10: Solving Data Engineering Challenges 14. Chapter 11: Infrastructure Provisioning 15. Chapter 12: Continuous Integration and Deployment (CI/CD) of Data Pipelines 16. Other Books You May Enjoy

Sharing data

In Chapter 1, The Story of Data Engineering and Analytics, we discussed the power of data. This has enabled organizations to realize revenue diversification using data monetization. But this dream cannot be effectively realized without sharing data with external parties. In the past, organizations used several data-sharing mechanisms such as emails, SFTP, APIs, cloud storage, and hard drives:

Figure 10.23 – State of data sharing currently

Unfortunately, there are several problems related to these data-sharing methods:

  • Complex: These data sharing mechanisms can be complex to set up and use because they may require exchanging keys/passwords and using a variety of different tools.
  • Insecure: These mechanisms may not be secure for data-at-rest or data-in-transit. This means the classic man-in-the-middle attack could expose data in cleartext.
  • Tracking: There is no clear method available for effectively tracking who shared data...
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