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Azure Data and AI Architect Handbook

You're reading from   Azure Data and AI Architect Handbook Adopt a structured approach to designing data and AI solutions at scale on Microsoft Azure

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781803234861
Length 284 pages
Edition 1st Edition
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Authors (2):
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Olivier Mertens Olivier Mertens
Author Profile Icon Olivier Mertens
Olivier Mertens
Breght Van Baelen Breght Van Baelen
Author Profile Icon Breght Van Baelen
Breght Van Baelen
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1: Introduction to Azure Data Architect
2. Chapter 1: Introduction to Data Architectures FREE CHAPTER 3. Chapter 2: Preparing for Cloud Adoption 4. Part 2: Data Engineering on Azure
5. Chapter 3: Ingesting Data into the Cloud 6. Chapter 4: Transforming Data on Azure 7. Chapter 5: Storing Data for Consumption 8. Part 3: Data Warehousing and Analytics
9. Chapter 6: Data Warehousing 10. Chapter 7: The Semantic Layer 11. Chapter 8: Visualizing Data Using Power BI 12. Chapter 9: Advanced Analytics Using AI 13. Part 4: Data Security, Governance, and Compliance
14. Chapter 10: Enterprise-Level Data Governance and Compliance 15. Chapter 11: Introduction to Data Security 16. Index 17. Other Books You May Enjoy

The VertiPaq engine for tabular models

The VertiPaq engine is an in-memory database engine that is responsible for storing the data model in memory. This engine leverages advanced compression algorithms and memory management techniques to fit the data model in memory:

  • Columnar compression: VertiPaq stores data in a columnar format, where each column is compressed independently. This allows for efficient compression based on the characteristics of the data in each column. Columnar compression eliminates redundant values and exploits data patterns within a column, resulting in significant space savings.
  • Hash or dictionary encoding: VertiPaq uses dictionary encoding to compress repetitive values within a column. It creates a dictionary that maps unique values to numeric identifiers, then replaces the actual values with these compact identifiers. As a result, the storage required for repeated values is greatly reduced.
  • Run-length encoding: This technique is used to compress...
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