[repost from Stephen Forte's Blog]
Part I we looked at the advantages of building a data warehouse
independent of cubes/a BI system and in Part II we looked at how to
architect a data warehouse’s table schema. In Part III, we looked at
where to put the data warehouse tables. In Part IV, we are going to look
at how to populate those tables and keep them in sync with your OLTP
system. Today, our last part in this series, we will take a quick look
at the benefits of building the data warehouse before we need it for
cubes and BI by exploring our reporting and other options.
said in Part I, you should plan on building your data warehouse when you
architect your system up front. Doing so gives you a platform for
building reports, or even application such as web sites off the
aggregated data. As I mentioned in Part II, it is much easier to build a
query and a report against the rolled up table than the OLTP tables.
demonstrate, I will make a quick Pivot Table using SQL Server 2008 R2
PowerPivot for Excel (or just PowerPivot for short!). I have showed how to use PowerPivot before on this blog,
however, I usually was going against a SQL Server table, SQL Azure
table, or an OData feed. Today we will use a SQL Server table, but
rather than build a PowerPivot against the OLTP data of Northwind, we
will use our new rolled up Fact table.
To get started, I will
open up PowerPivot and import data from the data warehouse I created in
Part II. I will pull in the Time, Employee, and Product dimension tables
as well as the Fact table.
Once the data is loaded into PowerPivot, I am going to launch a new PivotTable.
understands the relationships between the dimension and fact tables and
places the tables in the designed shown below. I am going to drag some
fields into the boxes on the PowerPivot designer to build a powerful and
interactive Pivot Table. For rows I will choose the category and
product hierarchy and sum on the total sales. I’ll make the columns (or
pivot on this field) the month from the Time dimension to get a sum of
sales by category/product by month. I will also drag in Year and Quarter
in my vertical and horizontal slicers for interactive filtering. Lastly
I will place the Employee field in the Report Filter pane, giving the
user the ability to filter by employee.
The results look like this, I am dynamically filtering by 1997, third quarter and employee name Janet Leverling.
is a pretty powerful interactive report build in PowerPivot using the
four data warehouse tables. If there was no data warehouse, this Pivot
table would have been very hard for an end user to build. Either they or
a developer would have to perform joins to get the category and product
hierarchy as well as more joins to get the order details and sum of the
sales. In addition, the breakout and dynamic filtering by Year and
Quarter, and display by month, are only possible by the DimTime table,
so if there were no data warehouse tables, the user would have had to
parse out those DateParts. Just about the only thing the end user could
have done without assistance from a developer or sophisticated query is
the employee filter (and even that would have taken some PowerPivot
magic to display the employee name, unless the user did a join.)
course Pivot Tables are not the only thing you can create from the data
warehouse tables you can create reports, ad hoc query builders, web
pages, and even an Amazon style browse application. (Amazon uses its
data warehouse to display inventory and OLTP to take your order.)
I hope you have enjoyed this series, enjoy your data warehousing.