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Data Lakehouse in Action

You're reading from   Data Lakehouse in Action Architecting a modern and scalable data analytics platform

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801815932
Length 206 pages
Edition 1st Edition
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Author (1):
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Pradeep Menon Pradeep Menon
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Pradeep Menon
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Table of Contents (14) Chapters Close

Preface 1. PART 1: Architectural Patterns for Analytics
2. Chapter 1: Introducing the Evolution of Data Analytics Patterns FREE CHAPTER 3. Chapter 2: The Data Lakehouse Architecture Overview 4. PART 2: Data Lakehouse Component Deep Dive
5. Chapter 3: Ingesting and Processing Data in a Data Lakehouse 6. Chapter 4: Storing and Serving Data in a Data Lakehouse 7. Chapter 5: Deriving Insights from a Data Lakehouse 8. Chapter 6: Applying Data Governance in the Data Lakehouse 9. Chapter 7: Applying Data Security in a Data Lakehouse 10. PART 3: Implementing and Governing a Data Lakehouse
11. Chapter 8: Implementing a Data Lakehouse on Microsoft Azure 12. Chapter 9: Scaling the Data Lakehouse Architecture 13. Other Books You May Enjoy

Methods of data masking in a data lakehouse

A data lakehouse can contain a lot of sensitive data that needs protection from unauthorized access. This could include Personally Identifiable Information (PII) such as social security numbers, email, or phone numbers, or sensitive information such as credit card or bank account numbers. Not everyone needs to access this sensitive data. A data masking service adds a layer of protection to ensure that sensitive data is only accessed by the most privileged users with the need to access it. Data masking is a way to create an artificial but practical version of data. It protects sensitive data without sacrificing the functionality that it offers. There are several reasons why data masking is vital for an organization:

  • Data masking mitigates external and internal threats. For example, data exfiltration, insider threats or account compromise, and insecure interfaces with third-party systems are some threats mitigated by data masking.
  • ...
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