Brian Cox
Sep 29 2011
When merging, watch your asterisk
by Brian Cox
Recently, while working with T-SQL's merge statement and its output clause I ran across an error I couldn't track down. My statement looked something like this:
Merge #target_1 as target using (select value1 from #source ) as source on target.value1 = source.value1 when not matched then insert (value1) values (value1) OUTPUT $action, inserted.* into #action_target;
The code was producing this error
Msg 213, Level 16, State 1, Line 25 Column name or number of supplied values does not match table definition.
Tagged: $action, *, merge, Microsoft, output, output clause, t-sql, transact sql, transact-sql
Mar 01 2011
LogiXML - Steps in the right direction, but still a ways to go
by Brian Cox
A few fellow CapTechers and I recently had the opportunity to attend on-site training for LogiXML's Business Intelligence tools. While the LogiXML Info and Ad Hoc tools seem fairly comprehensive, there is still room for improvement on the development end of things.
LogiXML's two BI tools are very similar. While the Info is more geared toward technical power users, the less powerful Ad Hoc option is more business user friendly. This seems to be fairly standard among most companies’ BI offerings and LogiXML is toeing that line fairly effectively. That said, most of our training focused on LogiXML Info and that’s where the majority of this post will focus.
Tagged: BI, Business Intelligence tools, LogiXML
Apr 30 2010
Data vs Information
by Brian Cox
The terms data and information are often used interchangeably. However, in the data warehousing world they are quite different from each other. Remembering the difference is as simple as the difference between Charlie and Raymond Babbitt. You may remember these two characters from the movie Rainman. Charlie is the younger brother of austitic savant Raymond.
Tagged: Data, Data Warehousing, Information
Mar 04 2010
Why use the native Unpivot transformation?
by Brian Cox
In my last post, I covered how to use an unpivot transformation in SSIS. There are, however, at least two other ways the same output can be achieved. In addition to the built in transformation, a multicast into a union all or a script task can be used. While all three methods produce the same dataset, the latter two are slower and one is much less efficient than the native transformation. If you are interested in each of the three methods, continue reading; otherwise, you can jump to the results.
Tagged: DMBI, Microsoft, SQL Server, SSIS, Unpivot
Jan 26 2010
Unpivot in SQL Server Integration Services (SSIS)
by Brian Cox
When importing data from a flat file into a database it is not uncommon to be presented with a source that has repeating data sets. This data isn't normalized and usually needs to be split into a single row for each repeating group. Moving multiple matching sets of columns into rows can easily be done using the Unpivot transformation in SQL Server Integration Services (SSIS). For example, the following data is presented containing 2 columns that apply to every field (Date and Type), followed by 5 groups of 3 columns (Salesperson, Sales and Units).
Tagged: DMBI, Microsoft, SQL Server, SSIS, Unpivot
Categories
Popular Tags
Disclaimer
The words and opinions expressed here are those of each article's respective author, and do not necessarily represent the views of CapTech Ventures.