Above is the input table. I am trying to bring all the childsku products in a single row with its count and its associated parent sku.But it is not working as expected           select [Parent SKU (PartNo) ],count([Child SKU]),[Child SKU]  from tbl_name        group by [Parent SKU (PartNo) ],[Child SKU]   Now I want the desired output to be like this:
I'm trying to migrate a MySQL-based app over to Microsoft SQL Server 2005 (not by choice, but that's life).  In the original app, we used almost entirely ANSI-SQL compliant statements, with one significant exception -- we used MySQL's group_concat function fairly frequently.  group_concat, by the way, does this: given a table of, say, employee names and projects...  SELECT empName, projID FROM project_members;   returns:  ANDY   |  A100 ANDY   |  B391 ANDY   |  X010 TOM    |  A100 TOM    |  A510   ... and here's what you get with group_concat:  SELECT      empName, group_concat(projID SEPARATOR ' / ')  FROM      project_members  GROUP BY      empName;   returns:  ANDY   |  A100 / B391 / X010 TOM    |  A100 / A510   So what I'd like to know is: Is it possible to write, say, a user-defined function in SQL Server which emulates the functionality of group_concat?   I have almost no experience using UDFs, stored procedures, or anything like that, just straight-up SQL, so please err on the side of too much explanation :)
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Some articles seemed to suggest Inverse Distance Weighting (IDW) as the most suitable technique, yet I seemed to get the worst results with that (possibly owing to low point density).  Are there any heuristics which can be employed here considering the size of the lake, its regular profile and the point sampling density to arrive at which interpolation method is apt? (between Kriging, IDW, Bilinear, Cubic convolution, TIN or spline)  Or is it always a bit of trial and error?