I am continuing with presenting different solutions for interval queries in SQL Server. For an introduction, please refer to the blog post Interval Queries in SQL Server Part 1. Note that you also need to read an excellent article by Itzik Ben-Gan wrote on interval queries in SQL Server (http://sqlmag.com/t-sql/sql-server-interval-queries) by using the Relational Interval Tree model. I am using the tables and data Itzik has prepared. In order to test the solutions, you can download the code from Itzik’s article by using the link in this paragraph.
My good old friend Itzik Ben-Gan wrote an excellent article on interval queries in SQL Server (http://sqlmag.com/t-sql/sql-server-interval-queries
) by using the Relational Interval Tree model. Based on the model developed by Kriegel, Pötke, and Seidl, and enhanced by Martin, Itzik fully developed a T-SQL solution. The solution is great, and makes interval queries efficient in all circumstances. However, the solution is quite complex. Itzik made a Microsoft Connect feature proposal (https://connect.microsoft.com/SQLServer/feedback/details/780746
) to add SQL Server Engine support for interval queries. I fully agree that this would be the best solution; now when the theory is known and implementation made possible, it is time that Microsoft puts this in the database engine. (more…)
In these times of weird economics in many places, more and more small and medium companies try to operate without a dedicated database administrator (DBA). Other companies still have one or more dedicated DBAs, however, (s)he or they is or are overwhelmed with work. Nevertheless, SQL Server and databases need regular monitoring and maintenance. SolidQ responded to these needs with a service called DB Flex. (more…)
This is just a very quick information about SQL Server 2012 Data Quality Services (DQS) and Master Data Services (MDS). If you don’t have a clue what they are useful for, this blog post should give you the basic idea.Many companies or organizations do regular data cleansing. When you cleanse the data, the data quality goes up to some higher level. The data quality level is determined by the amount of work invested in the cleansing. As time passes, the data quality deteriorates, and you need to repeat the cleansing process. If you spend an equal amount of effort as you did with the previous cleansing, you can expect the same level of data quality as you had after the previous cleansing. And then the data quality deteriorates over time again, and the cleansing process starts over and over again.
Bad database design and inefficient queries lead to applications that are hard to maintain and upgrade and that don’t perform well. Why don’t you start with a good design? Join me during the DevWeek 2013
post-conference seminar that provides developers and administrators with essential knowledge needed for a good database logical and physical design for well-performing applications in a single day. Of course, the knowledge gained by attending this seminar helps improving design and performance of existing databases as well.
Contingency tables are used to examine the relationship between subjects’ scores on two qualitative or categorical variables. They show the actual and expected distribution of cases in a cross-tabulated (pivoted) format for the two variables. Here is an example of the actual and expected distribution of cases over the Gender column (on rows) and the MaritalStatus column (on columns) of the dbo.vTargetMail view from the AdvetureWorksDW2012 demo database: