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SQL Server 2019 Administrator's Guide - Second Edition

You're reading from  SQL Server 2019 Administrator's Guide - Second Edition

Product type Book
Published in Sep 2020
Publisher Packt
ISBN-13 9781789954326
Pages 522 pages
Edition 2nd Edition
Languages
Authors (2):
Marek Chmel Marek Chmel
Profile icon Marek Chmel
Vladimír Mužný Vladimír Mužný
Profile icon Vladimír Mužný
View More author details

Table of Contents (17) Chapters

Preface 1. Section 1: Provisioning the SQL Server Environment
2. Chapter 1: Setting Up SQL Server 2019 3. Chapter 2: Keeping Your SQL Server Environment Healthy 4. Section 2: Server and Database Maintenance
5. Chapter 3: Implementing Backup and Recovery 6. Chapter 4: Securing Your SQL Server 7. Chapter 5: Working with Disaster Recovery Options 8. Chapter 6: Indexing and Performance 9. Section 3: High Availability and the Cloud with SQL Server 2019
10. Chapter 7: Planning Migration and Upgrade 11. Chapter 8: Automation – Using Tools to Manage and Monitor SQL Server 2019 12. Chapter 9: Configuring Always On High Availability Features 13. Chapter 10: In-Memory OLTP – Why and How to Use it 14. Chapter 11: Combining SQL Server 2019 with Azure 15. Chapter 12: Taming Big Data with SQL Server 16. Other Books You May Enjoy

Big data overview

Big data has garnered an immense following in the data industry and with SQL Server's entry, it is a new ball game altogether! In this chapter, we will explore the big data phenomenon, along with the part SQL Server plays in it.

Big data processing brings several challenges. The 5Vs of big data present those challenges:

  • Volume: This represents the quantity of the data. Just imagine that, during 2020, the digital universe is expected to reach the size of 40 ZB (zettabytes). The challenge here is to store, replicate, and consume such a huge volume. The next year, the volume will be larger again.
  • Veracity: This represents the quality of the data, which can vary greatly. Not every information system or source of data produces the same quality data. Data can be incomplete and have missing information or it can be inconsistent, and while processing such datasets, you must implement the data cleansing process to overcome such challenges.
  • Variety:...
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