My New Role at Fiserv: Patterns and Practices Manager

In addition to the Data Architect role that I will continue to play, I am now the Patterns and Practices Manager for the Digital Channels Enterprise Architecture team.

Here is the text right out of the March 12th e-mail that announced the change:

Rob will take on responsibility for managing our processes and best practices within the Group. He will continue to lead our Database design discipline, but will also take on management for the Corillian [Professional Services] Architects. Michael Hallabrin, Tim Sarna, and Rocco Martin will report in to Rob as part of the CO-L Solutions Architecture team.

I am really excited about this new role. Michael, Tim, and Rocco are a bunch of really smart guys, and I am enjoying working more closely with them.

Rob

Presenting on SQL Server Change Data Capture in Eugene

I will be speaking to the Eugene Area SQL Server User Group on CDC.

Topic: Deconstructing SQL Server's Change Data Capture

SQL Server's Change Data Capture feature was introduced with SQL 2008, but how much do you know about how it works? After attending this session, you will understand the various parts involved in CDC, know how to set it up, and know the relative performance effect.

Our focus here is not just showing how CDC works on the surface (there are many articles that cover that), but rather to see what we can find out about the various moving parts within SQL Server that actually make CDC function. Understanding the internals will help in an overall understanding of CDC.

Location/Time

Peace Health, 123 International Way
Springfield OR

April 9th at 11:30am

A Creative Use of Computed Columns

I love to see a creative solution to a technical problem.

How can I get that user out of my table quickly by JackLi

Great creative solution using indexed persisted computed columns.

The beauty of indexed persisted computed columns is that, yes, you have to pay the cost of doing the computation, but you only pay that cost at the point where you INSERT or UPDATE that particular column. If the data is mostly static as it is in this case, the one-time cost is very small.

You Can’t Sacrifice Partition Tolerance

Just finished reading this excellent article by Coda Hale:

You Can’t Sacrifice Partition Tolerance

I thought the bit about yield versus uptime was very interesting, as was the bit about harvest (harvest = data available/complete data). Historically, [the corporate] we have built systems that sacrifice yield in the event of a failure. I like the challenge/opportunity of building a system that reduces harvest but maintains yield.