Pre-con at SQL Saturday Richmond in March – Emergency Preparation for Database Disasters
On March 18th I will be attending SQL Saturday 610 in Richmond Virginia and the day before I will be presenting a pre conference session covering half a day on Database Corruption and half a day on Disaster Recovery Planning. This event has just been announced, and you can sign up for the pre-con session now. I will be presenting with my business partner and podcast co-host Carlos Chacon.
Emergency Preparation for Database Disasters
First half of the day on Database Corruption Preparation:
We will walk through several corrupt databases exploring ways to go about finding and fixing the corruption. More importantly we will explore how to prevent further data loss at the time corruption occurs. Learn what things you can do to protect yourself when corruption strikes. Learn how to avoid making things worse, and how to protect your data if things do get worse. You will leave with a checklist of steps to use when you encounter corruption. By the end of this session you will be ready to take on corruption, one database at a time.
Second half of the day on Disaster Recovery Planning:
How likely are disaster events to occur? How aware are you of the other risks you face? How prepared are you in the event that a disaster occurs?
In this session we will explore steps that go into building a solid disaster recovery plan, along with building a disaster recovery team to back up the plan.
When I have presented at PASS Summit and SQL Server users groups, I have often times received the feedback that I should expand my database corruption presentation into a pre-con session. Well here it is, a chance to go into much deeper detail that I have been able to do in previous sessions. I will present on several of the things that I have learned throughout my career, and with the database corruption challenge from 2015. The way to prepare for corruption is to practice finding and fixing corruption so that you will be ready when corruption strikes your environment.
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