2. Summary
This Portfolio contains samples from BI solutions developed using Microsoft
SQL Server R2 and the Microsoft Business Intelligence Toolset.
T-SQL Programming
MDX Programming
Integration Services ETL System (SSIS)
Analysis Services OLAP Database (SSAS)
Reporting Services (SSRS)
Sharepoint BI Delivery
PerformancePoint Services | Reporting Services | Excel Services
4. Freight Allocation
Specification: Using the Northwind Database Orders and [Order Details] table, create a result set that allocates freight downward
to all product line items, based on each product's dollars as a percentage of the dollars for the product as a whole. Validate this by
summing the allocated freight. Grand total of the summed freight is $64,942.69
In order to achieve the precise results needed for
this allocation, it was necessary to CAST the Partial Result Set
OrderTot as FLOAT. The OrderTot = UnitPrice *
Quantity. While Quantity has an INTEGER data
type, the UnitPrice has a MONEY data type. The
MONEY data type restricts any decimal to four
non-expanding decimal places, which creates a
problem for the allocation ratio. When using a
floating point the result is off by only
1/100,000,000 of a penny.
5. Top Five Vendors
Specification: Using the AdventureWorks2008 Database Purchasing.Vendor and Purchasing.PurchaseOrderHeader tables to show
the top five vendors for 2003. Generate a ranking number for each vendor and show the data by quarter.
Complete Result Set
The native T-SQL Pivot operator is used to display
the data with the VendorRank on rows.
The Pivot request involves three logical
processing phases with associated elements:
1) grouping phase
2) spreading phase
3) aggregation phase
Here the pivot table is grouped by VendorRank,
with the OrderDate quarter spread on columns
and the TotalDue to the vendor aggregated.
6. DYNAMIC SQL
This an example of using dynamic SQL when a pivoted results set when is needed, but the number of columns to be created is
not known in advance.
Partial Result Set
7. GET VENDOR PRODUCTS
This procedure gets the top @n Vendors within a specified date range, and ranks them by PurchaseOrderHeader.TotalDue DESC. The
results are stored in the @VendorTable variable. For each of the vendors, the top @y products within the same date range are
subsequently selected and ranked by (PurchaseOrderDetail.UnitPrice * PurchaseOrderDetail.OrderQtry) DESC. The final
result shows the top @n vendors and their top @y product sales.
Partial Result Set
Ranking is done using DENSE_RANK in
the OVER clause. DENSE_RANK indicates
how many distinct ordering rows have
lower values.
The CROSS APPLY operator is used to
return the products for the top vendors
by applying each row in the product
query to each row in the @VendorTable
variable.
8. USING T-SQL MERGE
Here the T-SQL Merge, which is new for SQL Server 2008, is used to insert new or update existing records in a production table
from a staging table in a data warehouse scenario.
The MERGE statement allows data to be
inserted, updated or deleted based on
conditional logic.
10. TOP FIVE CITIES WITHIN TOP FIVE MONTHS
Using the Adventure Works cube the top 5 cities were ranked within the top 5 months, based on the percent sales change over
the prior month.
Complete Result Set
To get the top 5 cities within the top 5 months based on the %
increase of the internet sales over the previous month , the top 5 RankedMonths (only)
months were determined first using the TOPCOUNT function and then
ranked using the RANK function.
The GENERATE function, which is specifically used as a means of
generating a set based on iteration over another set, was then
applied to the current [RankedMonths] member and another
TOPCOUNT was used to find the top 5 customer cities within each of
the top 5 months.
11. LAST 2 QUARTERS OF DATA FOR HAMBURG
This query uses the Adventure Works cube, on rows to show Hamburg and the states that have the same parent as Hamburg on
rows. Columns show the last 2 available quarters of data for internet sales and the percent of geographical parent.
Complete Result Set
The significance of this query is
that by using a FILTER for null
data combined with the TAIL
function, the last two quarters
of available data for the current
customer geography will always
be returned.
12. CALCULATING COMMON TIME BASED METRICS
The Adventure Works Cube is used here to show all customer country members across the columns and on rows show for June
and July of 2004 and then crossed with the following measures: Internet Sales Amount, Sales Amount Last Period, YTD sales, YTD
Sales for last year, Geographic % of Parent for Internet sales for the month and Geographic % of Parent for Internet sales for last
year.
Time-based expressions
shown here were combined
with other time based
expressions in order to
assemble more complex
metrics.
As an example, the [YTDLY]
member used the MDX
function PARALLELPERIOD
with the [YTD Sales] member
to determine the sales one
year ago
Complete Result Set
14. ALLWORKS CONSTRUCTION COMPANY PROJECT
AllWorks is a fictitious construction company that uses data stored in various formats as part of their
enterprise system. Employee and Client Geography data, along with Overhead and Job Order master
data are stored in spreadsheets. The Material Purchases data is exported from an Oracle database into
XML format, and Timesheet data is provided in .csv files.
For this project all data from the files were transformed and loaded to a normalized database using
SSIS. The Package Flow Design shows the input data flow into the SSIS packages. Each SSIS package was
named for the respective AllWorksOLTP database table that it loads.
AllWorksOLTP
Database
15. PROJECT SOLUTION
The project solution contains all the data load packages as well as Master package, and a Database Maintenance package The Master
package uses the Execute Package Task to run each of the packages in the proper sequence based, database foreign key
constraints, followed by the Database Maintenance package.
The Control Flow of each package generates an email notification for either the success or failure of the package. Success emails
include counts of files processed: rows inserted, rows changed and invalid rows. Package configuration is used to dynamically update
the database server, SMTP Server and mailbox variables based on the current runtime environment.
16. TIMESHEET PACKAGE CONTROL FLOW
The Timesheet Package Control Flow uses a Foreach Loop Container to loop through a variable number of .csv timesheet files in a
folder. A Script Task inside the loop container accumulates insert/update/error totals in variables, for each file and for the entire
folder. Script Tasks are used to write either a success or a failure email, as applicable. The Send Mail Task is used to send the email to
an SMTP server.
17. TIMESHEET DATA FLOW
The data for each record that moves through the Timesheet Data Flow pipeline, is first converted from a .csv file data type to a SQL
Server data type. The data is then checked using a Lookup Transformation to verify that ProjectID and EmployeeID are valid. A
Conditional Split Transformation is used to make sure the project has not been closed. If the project is not closed and the ProjectID
and EmployeeID are valid then a Lookup Transformation is used to determine if the Employee Time sheet already exists in the
database, and if not the timesheet record is inserted, otherwise the timesheet record is updated. A Conditional Split is used so that
only modified timesheet data is sent to the database. An OLE DB Destination is used to insert data. An OLE DB Command is used to
update data.
18. LOOKUP TRANSFORMATION
A Lookup Transformation Editor is used to validate the ProjectID and also to bring the Project Closed Date data into the dataflow
pipeline.
19. C# SCRIPTING FOR ACCUMULATING TOTALS
The Microsoft Visual Studio Tools for Applications (VSTA) was accessed using the Script Task. C# scripting is used to accumulate the
Timesheet record count totals.
Variables used by the
Script Task were first
created in the SSIS
package.
Read- write variables were selected for
use in Script Task Editor from the SSIS
package variables. These variables
were then available to be used within
the code of the Script Task through the
Dts.Variables collection, which
Integration Services automatically
creates and makes available to the
script code.
20. MASTER PACKAGE
Execute Package Tasks with precedence constraints that mirror PK/FK database constraints were used to execute the individual
packages in the ETL solution. Upon successful completion of the ETL, database maintenance is performed.
21. DATABASE MAINTENANCE PACKAGE
The Database Maintenance Package shrinks the database, rebuilds the database indexes, updates the database statistics and backs-up
the database nightly following successful completion of the Master Package.
24. DATA SOURCE VIEW
SQL Views were scripted in the AllWorks OLTP database to generate the dimensions and measures needed to create the AllWorks
dimensional database in Analysis Services. In SSAS a data connection to the SQL Server relational database was established and a data
source view (DSV) created using the dimensional views. In the DSV, entity relationships and logical primary keys were identified.
The JobClosdedDate in the
vwDimProject view is used in a role
playing dimension, and therefore an
entity relationship between
vw.DimProjct.ClosedDateKey and
vwDimDate.WeekendKey is needed.
A named calculation was created to
provide a description for the Boolean
value returned by the Employee Flag
in the vwDimEmployee view.
25. ALLWORKS DW CUBE
A cube consisting of four measure groups and five dimensions was then generated using the Cube Wizard. The Date dimension is
used in the cube twice as a role-playing dimension for the Project Closed date.
26. DIMENSION USAGE
Relationships in the cube between the dimensions and
measures was verified by inspecting the Dimension
Usage tab. A referenced relationship was manually
created for the intersection between the Project
Closed Date (role playing dimension) and the Summary
Measure Group.
This dimension usage tab from another cube shows a many-to-
many relationship between BookSales and Authors. Each Author
can have multiple Books for sale, and each Book may have more
than one Author. This necessitates the intermediate cross
reference table, BookXAuthors.
27. DIMENSIONS
All cube dimension
attributes and user-
defined hierarchies. The Dimension Structure Tab was used to specify each dimension’s attributes and user-defined
hierarchies. Here a date hierarchy was built using the Year, Quarter and Weekend date.
Rigid attribute relationships were
created to associate the attributes
used in the Date Tree hierarchy.
Setting the RelationshipType property
determines whether Analysis Services
creates rigid or flexible aggregations.
After an incremental update, Analysis
Services drops flexible aggregations and
those aggregations must be manually
reprocessed, but Analysis Services
persists rigid aggregations, which
improves query performance!
28. BROWSING THE CUBE
After the cube was deployed and processed successfully, the Cube Browser dimensions and measures were selected from the
metadata pane and the results were examined against the original OLTP database for accuracy. The cube can also be easily browsed
using an Excel Pivot Table.
29. CALCULATIONS
MDX expressions were used to create calculated members and named sets.
This calculated member computes
the open receivables as a percent of
invoice amount.
It is also rendered here as a Excel
pivot table which uses it along with
the Invoice Amount measure and
the Open Receivables calculated
member, for the All Clients named
set in 2006, broken out by quarter.
30. KPI’S
Key performance indicators (KPIs) were created using the newly deployed calculated members for the KPI value expression. In this KPI
a Traffic Light is used to visually represent the Profit Percent metric, with a goal of greater than 15% profit. The KPI is displayed for all
clients using MS Excel. Green indicates the goal of greater than 15% profit for the client’s projects have been met. A yellow traffic light
indicates that the profit percentage is between 15% and 5% inclusive. A red light indicates that the profit is less than 5%. No traffic
light is displayed for clients who do not have any closed projects.
While the KPI Goal
here is static, it can
also be data driven
31. ACTIONS
A report URL action was created so that the client application can execute a live Google Maps search based on the project’s county.
32. PARTITIONS AND AGGREGATIONS
Partitions were created for each fact table to separate current from historical data . MOLAP storage with aggregations designed for a
50% performance increase were specified.
Aggregations are
precalculated
summaries of data from
leaf cells
34. MOVING AVERAGE OLAP REPORT
This report shows sales dollar revenue as a column bar and the 12 month moving average as a horizontal line. The user can select one
or more years, and a product category, subcategory or product. The Fiscal Year dropdown excludes the first year of sales.
35. SALES BY CATEGORY EXPLODED PIE CHART
This Exploded Pie Chart report shows sales proportions by category. Here the legend is hidden and an expression is used to label each
pie slice with the Category, Sales Dollar Amount and the % of Total. The labels are configured to display outside of the pie for
readability purposes.
36. EMPLOYEE SALES QUOTAS W/ SPARKLINES & GAUGES
This is an employee sales matrix OLAP report. The gauges give an ‘at a glance’ indication of each employee’s sales in comparison to
their quota, while the sparklines provide a visual representation of each employee’s sales vs quota trend.
37. TOP (N) PRODUCTS & TOP (Y) CITIES OLAP REPORT
This report allows the user select to the TOP N Products by Revenue, and within each product the TOP Y Cities. The user can select one
or more years. This report uses the MDX GENARATE function to achieve the TopY within the TopN.
39. CONTOSO RETAIL DASHBOARD
The Contoso Retail Dashboard project is comprised of six PerformancePoint pages deployed to SharePoint 2010. An additional SSRS
report with a delivery subscription is also included. Performance Point Content
Site Collection
Dashboard Pages
40. KPI SCORECARD W/PROFIT MARGIN HOTLINK REPORT
The first dashboard page contains two objective KPIs (Financial and I.T. Systems) and 4 KPIs (Product Gross Profit Margin, Channel
Revenue, Returns Pct and Machine downtime trend). Each KPI has a “hot-link” report. The Financial KPIs show all Product Categories
underneath. The KPI score card and associated charts were created using the PerformancePoint Dashboard Designer.
The Product Gross Profit Margin KPI is linked at to the
chart by the filter date, filter geography and product
category selection. The hot-link chart shows monthly
sales, gross profit margin and gross profit margin for the
past year.
41. KPI SCORECARD W/SALES DRILL DOWN TO BRAND
Charts can be drilled down into for more granular information. This is a drill down for the TV and Video Product Category to see which
Brands are being sold.
42. KPI SCORECARD W/CHANNEL SALES HOTLINK REPORT
The Channel Revenue KPI is linked at to the chart by the filter date, filter geography and current product category selection. The hot-
link chart shows the monthly sales quota amount, the sales amount and the sales amount last year.
43. KPI SCORECARD W/RETURNS % HOTLINK REPORT
The Returns Percent KPI is linked to the chart by the filter date, filter geography and current product category selection. The hot-link
chart shows the sales return amount and the sales return percent for the ten stores with the most returns.
44. KPI SCORECARD W/HOTLINK SUPPORTING GRID
The Machine Downtime KPI contains a hot-link to an analytic grid, that breaks down Machine Down Time, Machine Down Time Last
Year and the Trend Outage Type by Fiscal Year and Geography.
45. PRODUCT SALES PROFILE WITH SIBLINGS REPORT
The second page of the Dashboard contains a PerformancePoint Analytic Chart of Product Monthly Sales for the selected Fiscal Year,
along with the Product Sales as a percent of the product’s hierarchical parent. A Supporting Grid also displays the Monthly Product %
of Parent Sales for all siblings of the Product selection.
46. EMPLOYEE PROFILE W/DRILL DOWN TO
DECOMPOSITION TREE
The third page of the Performance Point Dashboard contains an Analytic Chart of Sales as a % of Quota, by Employee(s) and months for
a year. A drill down to a decomposition tree for employee Kim Abercrombie, is used to display her subordinates and their % of Sales
Quota, as well as how they rank in relation to their peers.
Decomposition trees can
be used for root cause
analysis.
47. SSRS SALES MAP AND RETURNS % REPORT
The interactive Sales Map was created in SSRS and deployed to SharePoint. Here PerformancePoint acts as a wrapper for the SQL
Server Reporting Services report, so that the report can be displayed in the dashboard. The PerformancePoint Fiscal Year and Sort
Option filters are linked to the map and chart, which show Sales Amount by State. Additionally, the chart plots the Sales Return
Percent on a second vertical axis.
48. SSRS SALES MATRIX
The fifth page of the Dashboard is an SSRS Matrix report with two adjacent column groups for Sales by Retail Channel and Promotion,
and Row Groups for Product Category/Subcategory with drilldown and % of total functionality.
49. EXCEL SALES REPORT WITH SLICERS
A Pivot Table with Slicers for Product Category/Subcategory, Sales Chanel and Fiscal Year was created in Excel 2010 The Pivot Table
shows Sales Amount, Sales Return Amount, Gross Profit Margin and Sales Return Percent by the Geography Hierarchy. The report was
saved to SharePoint from Excel and then utilized by PerformancePoint to create the last page of the Contoso Retail Dashboard.
50. SSRS TOPS SELLING STORES SUBSCRIPTION REPORT
This is a simple Reporting Services Report deployed to Sharepoint. The report was set up to run nightly for the states of Maryland and
Virginia and be delivered to a Sharepoint folder.
51. Pamela Staerker
SetFocus Master’s BI Program 2011-2012
Instructor: Kevin S. Goff, Microsoft SQL Server MVP
http://www.linkedin.com/in/pstaerker
