In this article we shall demonstrate using Microsoft SQL Server 2005/2008 OPENQUERY AND OPENROWSET to add, delete and update data in PostgreSQL.
First we must start by saying there are a number of ways of copying data between databases. While OPENROWSET is not necessarily the fasted,
in certain cases such as when you are wrapping this in a stored procedure, it is one of the most convenient ways of doing this.
Why on earth would you want to copy data back and forth between 2 servers and 2 disparate DBMS systems for that matter?
We all would like to think we are an island and live in a world with one DBMS system, but we don't. There are many reasons for having multiple DBMS providers in
an organization. Some are better for some things than others, some are more integrated in an environment -- (for example in a windows shop the SQL Server drivers are already loaded on all
windows machines, but PostgreSQL provides the advantage of being able to run on more platforms such a FreeBSD/Unix/Linux box and with cheaper cost and more options for PL programming so is often better for a front-facing DMZ accessible database),
and there are numerous other reasons that are too hard to itemize. The other question of why triggering from SQL Server instead of PostgreSQL is because
its just a little easier from Microsoft SQL Server. The OPENROWSET and OPENQUERY logic that SQL Server provides is just simply better and easier to use than the dblink provided for PostgreSQL. Anyrate with that said lets move on with the show.
Although this example is focused on using PostgreSQL with Microsoft SQL Server, the same technique applies when
copying retrieving updating data from other databases such as MySQL or Oracle or DB II.
In our August 2008/ September 2008 issue we demonstrated the power of PostgreSQL to create median and MS Access-like first and last aggregate functions in
SQL language. In this article we shall demonstrate how to create aggregates with Python. We shall
call this function agg_plot. What it will do is plot each grouping of data and return a plot for each grouping. The steps
we covered in those articles can be applied here.