(Andrew J. Brust) The SQL Server relational engine matured a long time ago. There have been advances, of course, in performance, fault tolerance and high availability, not mention encryption, compression and file-system integration.

(James Serra) When designing a data warehouse, how you handle changes to dimensional data over time is the most important decision to make. It is rare that a dimension will remain static over time. For example, a customer may change their phone number or their address, or a sales person may change

(Marcin Policht) Extraction, transformation, and loading’s primary objective is to facilitate moving data between its source and destination while subjecting it to arbitrary modifications, and has been an integral part of the Microsoft SQL Server product line for a number of years.

(Vadivel) If we have an IDENTITY column and if our insert statement is trying to exceed the maximum value of INTEGER then it would throw this error.

(Josh Robinson) As part of a project I am working on that requires me to programmatically alter existing packages, I ran into an annoyance (bug?) in SSIS that makes it hard to open packages after altering them.

(Ben Snaidero) All DBA’s perform some sort of monitoring of their SQL Server database instances as no one likes to find out from a user that there is an issue with the database. In an ideal world we would notice any issues before they occur and have a fix in place so the users of our systems don’t e

(Richard Carr) Transact-SQL does not provide a standard function that allows developers to determine if a year is a leap year. This article describes two functions. One to calculate the number of days in a year and one that utilises the result to identify leap years.

(Brent Shaub) Frequently surveys ask yes/no questions and use a bit datatype. What are the effects of allowing this column to be NULL in a SQL Server table? Check out this tip to learn more.

(Michael K. Campbell) In my last post I provided an overview of what SQL Server database corruption is – and how it’s almost always caused by problems at the IO subsystem (or disk) level. However, while it’s all fine and well to talk about things in such a theoretical sense, in that post I also ment

(Denny Lee) Dorky attempts at geek Shakespere aside; as the volume, complexity, and variability of your data systems increase in … entropy …, this becomes a fundamental question in whether one scales up or scale out their data problem.