Recovery Time Objective & Recovery Point Objective

No one wants to lose data when disaster strikes; however, we need to plan for some data loss and putting all the pieces in place can be tricky. There are two metrics commonly used to measure data loss and availability when it comes to planning for disasters—Recovery Point Objective and Recovery Time Objective. In this episode, we touch on how you might come up with these metrics, how to consider the implementation cost, and how you might go about walking through a disaster situation to test your plan.

3 Takeaways

  1. Recovery Point Objective indicates the amount of data loss in time (not size).
  2. Recovery Time Objective indicates the time from disaster to recovery.
  3. Tabletop discussions may be the easiest way to approach recovery plans.

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“You can’t have any data loss.” And then you give them the bill for how expensive it is to ensure that there is never any data loss.

Kevin Feasel

Meet the Hosts

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Carlos Chacon

With more than 10 years of working with SQL Server, Carlos helps businesses ensure their SQL Server environments meet their users’ expectations. He can provide insights on performance, migrations, and disaster recovery. He is also active in the SQL Server community and regularly speaks at user group meetings and conferences. He helps support the free database monitoring tool found at and provides training through SQL Trail events.

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Eugene Meidinger

Eugene works as an independent BI consultant and Pluralsight author, specializing in Power BI and the Azure Data Platform. He has been working with data for over 8 years and speaks regularly at user groups and conferences. He also helps run the GroupBy online conference.

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Kevin Feasel

Kevin is a Microsoft Data Platform MVP and proprietor of Catallaxy Services, LLC, where he specializes in T-SQL development, machine learning, and pulling rabbits out of hats on demand. He is the lead contributor to Curated SQL, president of the Triangle Area SQL Server Users Group, and author of the books PolyBase Revealed (Apress, 2020) and Finding Ghosts in Your Data: Anomaly Detection Techniques with Examples in Python (Apress, 2022). A resident of Durham, North Carolina, he can be found cycling the trails along the triangle whenever the weather's nice enough.

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