English | Size: 239.99 MB
Genre: eLearning
Learn how to optimize the performance of your SQL Server backups using data science techniques. Microsoft provides default backup settings which work but which are decidedly sub-optimal against large databases. This video shows what these settings are, explains their defaults, and educates you on the available values that provide you room in which to optimize your backup times. The end game is that you’ll be able to optimize your backups and be confident that you’ve optimized to the extent that’s possible given your specific environment.
After an introduction to the available settings, you’ll learn a method built for tuning your backup settings to provide faster backups and shorter maintenance windows in your own environment. That method includes knowing how to sample timing data and other statistics, and how to apply data science techniques by which to understand your findings and to implement your findings in two of the most popular, community-based backup tools: MinionWare’s Minion Backup and Ola Hallengren’s SQL Server Maintenance Solution. You’ll be able to confidently schedule maintenance windows and to defend those schedules to management using the data that this video helps you to collect.
What You Will Learn
Know the key settings that control SQL Server backup performance
Create database backup statements using these key settings
Use sampling within PowerShell to test performance of different settings
Create models using algorithms such as random forest for statistical learning
Analyze the results of these models using evolutionary programming techniques
Implement the results using free, community-based backup solutions
Who This Video Is For
Database administrators who wish to minimize the size of their backup windows and those wanting to get their backups done in the least-possible time. Viewers should be familiar with SQL Server and the T-SQL language. The video will also include PowerShell and R, so familiarity with either of these languages is nice but not required.
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