William Berth Bi Portfolio

Business Intelligence
Portfolio

SetFocus
BI Masters Program William Berth
wberth@gmail.com
Design and Development of a Business
Solution using 802-375-9490
Microsoft Business Intelligence tools

This portfolio describes the training & contains examples of my project development skills
completed in the Business Intelligence arena using Microsoft SQL Server 2008 & Microsoft Visual
Studio 2008 while participating in the SetFocus Business Intelligence Master’s Program .
• About The Master's Program
• Advanced Transact SQL Project
• SQL Server Integration Services Project
• SQL Server Analysis Services Project
• SQL Server Reporting Services Project (including PerformancePoint Server (PPS), Excel, and
SharePoint (SP))
• Full Business Intelligence Implementation - Final Team Project

• The SetFocus BI Master’s Program is an intensive, hands–on, project oriented program allowing
knowledge and valuable experience to design and develop business intelligence systems using the
Microsoft Business Intelligence tools, part of Microsoft's SQL Server Applications, putting the BI
skill set to use in a simulated work environment with an emphasis on business application context.
• Students receive 400 hours of in-depth hands on experience including 7 tests, 5 projects, exercises,
workshops, & labs focused on the Microsoft Business Intelligence stack.
• Course topics include (but not limited to):
– Creating queries using advanced Transact SQL functions in SQL Server Management Studio.
– Designing, Implementing and Maintaining an ETL Solution Architecture using SQL Server Integration Services.
– Data warehousing (relational & multidimensional) and dimensional modeling concepts.
– Designing, Implementing and Maintaining an Analysis Solution Architecture Using SQL Server Analysis
Services, expanding your knowledge on KPI s, data mining, & MDX Queries .
– Creating complex reports employing expressions, global collections, and conditional formatting using SQL
Server Reporting Services.
– Plan, build, and roll out an Enterprise Reporting Dashboard utilizing SharePoint 2007 and leverage
Performance Point Server 2007 for monitoring and analysis purposes.
• Projects are completed individually. A full Business Intelligence project is completed as part of a
team. Project specs include deliverables, documentation, and a due date deadline.

1/3/2013 SetFocus: Business Intelligence Master's
Program: 2010I3 Back to TOC

• Introduction: Having a thorough understanding of T-SQL programming skills is essential for BI
applications and creating a data warehouse. The T-SQL session of the course focused on intermediate and
advanced T-SQL 2008 database programming concepts, database patterns, and reusable approaches. Some
of the topics covered included: SQL Server 2008 data types; partial text searches with LIKE and %; using the
GROUP BY to aggregate data; creating stored procedures, database views and database triggers;
implementing database transactions; implementing error handling with TRY/CATCH; advanced uses of PIVOT
statement, APPLY operator, GROUPING SETS, and RANK functions for reporting; change data capture and
database update triggers for audit trail logging; querying hierarchical data with recursive queries and the
Hierarchy ID data type; using subqueries and common table expressions; date handling and temporal-based
queries; error handling, transactions, and transaction isolation levels; Dynamic SQL and Dynamic SQL
alternatives; interpreting and analyzing execution plans; geo-spatial & recursive queries, and XML handling
as parameters.

• Overview: The T-SQL project put to use many of the concepts presented above. This project was
completed by each student individually. The project consisted of writing 10 queries in SSMS, each query
having its own specification including filters. Solutions for the project are contained on the following slides.
Additional SQL examples are provided in the SSIS and SSAS projects for building the data warehouse.

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• Query 1 Specification: Summarize LineTotal in Purchase Order Details as TotDollars by Product.
Show only those products with the word ‘Washer’ anywhere in the name. Retrieve based on the Order Date
Year of 2003.

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• Query 2 Specification: Count orders by Product Subcategory. Retrieve based on the Order Date Year of
2004. Only show those product subcategories with at least 10 orders.

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• Query 3 Specification: Show a list of vendors who did NOT have an order in 2003 (based on the year of
orderdate). Sort order doesn‘t matter.

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• Query 4 Specification: Summarize Freight by Ship Method Name. Retrieve based on first quarter of
2003. Show all Ship Methods, even if no data exists.

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• Query 5 Specification: Summarize TotalDue from Purchase Orders for the months in 2003. Retrieve for
the following National ID Numbers: (792847334, 407505660, 482810518, 466142721, 367453993). Use
DateName for month, but make sure to sort chronologically (make sure to match the sort in the output).

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• Query 6 Specification: Retrieve Names and Addresses from the vVendorWithAddresses view (and
defines a record type of ‘Vendor’). Retrieve Name and Addresses from the vEmployee View (concatenate
LastName, FirstName, Middle Initial). Convert any null values (Middle Initial, AddressLine2) to an empty
string. Sort the final output on PostalCode, and Name within PostalCode.

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• Query 7 Specification: Write a STORED PROCEDURE called GetVendorOrders that does the following:
Retrieves a Vendor Account Number and StartDate/EndDate parameter date range. Summarizes freight, tax
amount, and SubTotal by Ship Method, for the vendor and date range. Run the stored procedure for vendor
'ADVANCED0001' & date range '1-1-2003' to '12-31-2003'.

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• Query 8 Specification: Write a query that retrieves the top 5 week ending dates for 2003, based on
overall TotalDue in PurchaseOrderHeader (and rank them; the Grand total is the sum of all the Ship
Methods). Summarize all dates for the week ending (Saturday) date. Break apart sales by the Ship Methods
in the ShipMethod table.
-- Check if Saturday date function exists. If so, drop it.
IF EXISTS
(SELECT * FROM sys.objects
WHERE object_id = OBJECT_ID(N'[dbo].[SaturdayDate]')
AND type in (N'FN', N'IF', N'TF', N'FS', N'FT'))
DROP FUNCTION [dbo].[SaturdayDate]
GO

--Create Saturday date function
create function [dbo].[SaturdayDate] (@CurrentDate datetime)
returns datetime
as begin
declare @SaturdayDate datetime
SET @SaturdayDate = DATEADD(d, (7 - DATEPART(dw,@CurrentDate)), @CurrentDate)
return @SaturdayDate
end
go

-- This CTE selects all Saturday dates for order dates, amounts, & ship methods in 2003.
;with ShipMethodCTE as (
select dbo.SaturdayDate (OrderDate) as WeekEnding, poh.ShipMethodID, poh.TotalDue
from Purchasing.PurchaseOrderHeader poh
where year (poh.OrderDate) = 2003 ),

-- This CTE pivots (transposes) the ship methods from the 1st CTE.
PivotCTE as (
select WeekEnding, [1] as XRQ, [2] as ZY, [3] as OVERSEAS, [4] AS OVERNIGHT, [5] AS CARGO
from ShipMethodCTE
PIVOT (sum(TotalDue) for ShipMethodID in ([1], [2], [3], [4], [5] )) as temp )

-- This query selects, sums, & ranks the CTE.
select top 5 WeekEnding, sum(XRQ + ZY + OVERSEAS + OVERNIGHT + CARGO) as GrandTot,
rank () over (order by (sum(XRQ + ZY + OVERSEAS + OVERNIGHT + CARGO)) desc ) as WeekRank
,XRQ, ZY, OVERSEAS, OVERNIGHT, CARGO
from PivotCTE Back to TOC
group by WeekEnding, XRQ, ZY, OVERSEAS, OVERNIGHT, CARGO

• Query 9 Specification: Write a stored procedure that will receive four values: Top N Vendors, Top Y
Products within each Vendor, a Start Date, and End Date range. Get the top N Vendors and rank them by
POHeader.TotalDue descending so that no gaps occur. For each of the vendors, run a subsequent TOP query
to get the top Y products FOR THAT vendor. Note: some vendors only deal with one product. Run the
procedure with the parameters of 5, 5, and for the date range of Jan 1 2003 to June 30, 2004.

Click here to see query code
-- Execute procedure
exec TopVendorProducts 5, 5, '1-1-2003', '6-30-2004'

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• Query 10 Specification: Retrieve Products and Standard Cost, for those products with a standard cost
as of 1/1/2002 of $700 or more.
IF EXISTS (SELECT * FROM sys.objects
WHERE object_id = OBJECT_ID(N'[dbo].[StdCostDate]')
AND type in (N'FN', N'IF', N'TF', N'FS', N'FT'))
DROP FUNCTION [dbo].[StdCostDate]
go

-- This function determines the Standard Cost for a
-- product as of a specific point in time.
CREATE FUNCTION [dbo].[StdCostDate]
(@AsOfDate AS datetime, @CostAmt AS INT)
RETURNS datetime
AS BEGIN
declare @StdCostDate datetime
set @StdCostDate =
(select MAX(StartDate)
from Production.ProductCostHistory
where StartDate <= @AsOfDate
and StandardCost > @CostAmt)
return @StdCostDate
END
go

select pp.ProductNumber, pp.Name, round (pc.StandardCost,2) as StandardCost
from Production.Product pp
join Production.ProductCostHistory pc on pp.ProductID = pc.ProductID
where StartDate = dbo.StdCostDate ('1-1-2002', 700) Back to TOC
and pc.StandardCost > 700
order by pc.StandardCost desc

• Introduction: A key component to building a robust data warehouse is the ability to extract, transform,
and load (ETL) operational source system data (often disparate) and consolidate that data into a data staging
area. The SQL Server Integration Services (SSIS) BI tool provides the capability to read data from/write data
to different output formats (thus the name “integration services”, because of the integration with different
file systems). The SSIS session of the course focused on the fundamentals of SSIS, some best/recommended
practices in SSIS, and common design patterns in SSIS. Some of the topics covered included: data and control
flow task operations, variables, configurations, event handling and logging, transformations, package
management and deployment, SSIS scripts, running SSIS scheduled jobs using SQL Server Agent, and
integration of T-SQL Stored procedures with SSIS. Additionally, the course covered the new features in SSIS
2008 and all new MS BI enhancements in SQL Server 2008.

• Overview: The SSIS project is for a fictitious construction company called AllWorks. Students design and
build a SQL Server 2008 database to track employee and customer information, timesheet and labor rates
data, as well as job order information, job materials, and customer invoices. In the client project scenario,
AllWorks currently stores this information in Excel Spreadsheets, XML files, and CSV files. The first task is to
review the source data from the three different sources. Students are asked to make some enhancements to
the existing data sources, to support more flexible business practices for customer invoicing. Next, students
use SQL Server 2008 Integration Services to integrate these external data sources into the SQL Server
database. The goal is to create a 3NF database to hold all the data in the source files. A T-SQL script that
creates the AllWorks database, tables, database diagram and PK/FK constraints is included. This project was
completed by each student individually. Specifications and solutions are provided on the following slides.

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• Specifications: AllWorks currently uses spreadsheets and Oracle data (exported as XML) as part of their
systems. They store Employee and Client geography data, along with overhead and job order master data,
and invoices in spreadsheets. The feed of material purchases comes from an XML file. And finally, the
timesheet data comes from CSV files. Each individual file specification will be addressed in the following
slides. Some solution examples are also provided on the following slides. Generally, each package will have
similar processing except for the timesheet file (see this document for technical project specs):
The next step is to create a SQL Server OLTP database and transfer the data from the different
raw data sources (XLS, CSV, and XML). A data model was produced and a SQL script was built to create the
database and tables as shown in this document: Initially a full load of the data into the SQL database
will be executed, and then scheduled packages will be run nightly to import/update any additional
information.

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This is the relational database built
for AllWorks from the DDL scripts.

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• Employees Input File: This employees spreadsheet contains 2 worksheets. First sheet (employees) contains roster of Employees, and a
flag for whether the employee is a contractor or a regular employee (some overhead rates only apply to employees). Second sheet (employee
rates) contains Employee Hourly Rates for each employee, along with an effective date.
• Client Geographies Input File: This client /customer spreadsheet contains 2 worksheets. First sheet (Client Listing) contains each client
for AllWorks, along with a CountyKey to associate the client with a county. Second sheet (County Definitions) contains the list of counties.
• Overhead Input File: This overhead spreadsheet contains 2 worksheets. First sheet (Overhead master) lists each labor overhead category.
Second sheet (Overhead rates) lists the hourly rates for each overhead category, the rate effective date, and whether the rate applies to
employees and/or contractors. This relates to the employee flag in the employee table. Remember that a rate may change from applying to only
employees, to applying to both employees and contractors (or vice-versa).
• Project Master Input File: The job spreadsheet contains 1 worksheet which contains one row for each Job work order. It contains a
reference to the client, material markup %, labor overhead markup %, whether the job is closed, and the creation date. The markup % is
optional: the way they work is as follows: if $1,000 of material is purchased, and the Job markup % is 10 (10%...they are stored as whole
numbers), then the total material cost is $1,100. The same concept applies for labor markup, based on the # of hours of labor for the job.
• Invoices Input File: An Excel spreadsheet that contains client invoices, a link to the job number, the invoice amount and invoice number,
the paid amount, and any additional labor. (Sometimes AllWorks will append a lump sum amount). IMPORTANT NOTE! AllWorks invoicing
spreadsheet only allowed for one work order job per invoice (a 1-1 relationship between invoice and job ). AllWorks wants to expand that, so
that a single invoice can cover multiple jobs. Additionally, when AllWorks receives payment, they want to track how much was received for each
job on each invoice. So a 1-many link/cross-reference table is created, both for the invoice and the jobs associated with it and then another 1-
many table for each invoice receipt and the amount paid for each job on the invoice.
• Material Purchases Input File: This XML file contains the material purchase transactions for each job. It contains a link to the Job
number, the purchase amount, and the purchase date. AllWorks does not have a separate table for the TypeCode, they have been hardwiring it.
1 stands for Material, 2 stands for Petty Cash, and 3 stands for Fuel.
• Employee Time Input Files: There are multiple CSV files to be processed that contains all the labor data transactions – the employee id,
the work date, number of work hours, and the job number.

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• AllWorksOLTP Project Solution: The project solution contains 13 packages; 7 package details are described on subsequent slides. The
OverheadMaster, OverheadRates, Client, Project, InvoiceJob, & InvoiceReceipt packages are simple in design and not described in this portfolio.
They simply read different worksheets; validate ID fields; insert, update, or write out an flat error file based on the validation of the ID fields.

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• Employee Table Spec: Take the spreadsheet, Employees.XLSX, from the C: folder, and create a package that will read the contents of the
spreadsheet and add them to the SQL Server AllWorks OLTP database. The package must be able to insert missing rows based on the employee
ID. The package must be able to update existing rows. A Fullname column (FirstName then Lastname) must be created in the package.

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The employee
first name &
last name are
concatenated
together to
create a new
field called Full
Name.

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The input row is
checked against
the lookup alias.
If the input
employee ID does
not exist in the
lookup alias, the
row is inserted
into the database.
If the employee ID
lookup alias does
exist, input fields
are checked
against the lookup
alias fields. If data
has changed
the row is updated
in the database. If
not, the row is
invalid & is written
to a file.

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• Employee Rate Table Spec: Take the spreadsheet ,Employees.XLSX, from the folder C:, and create a package that will read the contents
of the spreadsheet (the 2nd sheet, for Employee Rates) and add them to the SQL Server AllWorks OLTP database. The package must be able to
insert missing rows based on the employee ID and Rate Effective Date. The package must be able to update existing rows. The student must
validate in the package that each employee ID in the employee rates spreadsheet is a valid employee ID. The package should write out a log of
any invalid employees (an employee ID that does not exist) to a flat file.

NOTE: All packages move
processed files to an output
area when the input file has
completed all processing
and emails are sent out
based on a package‟s
success or failure.

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Validate the employee id in the rate worksheet by matching it
against the employee table.

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The input row is
checked against the
lookup alias. If the
input employee ID
and effective date
does not exist in the
lookup alias, the row
is inserted into the
database. If the
employee ID &
effective date lookup
aliases do exist, input
fields are checked
against the lookup
alias fields. If data
has changed the row
is updated in the
database. If not, the
row is invalid & is
written to a file.

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The OLE DB Command task
to update the Employee Rate
table.

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• County Table Spec: Take the spreadsheet, Clientgeographies.XLS, from the folder C:, and create a package that will read the contents of
the spreadsheet (the 2nd sheet, for County Definitions) and add them to the SQL Server AllWorks OLTP database. The package must be able to
insert nonexistent rows based on the County ID. If the Description column contains the word “County”, remove it and add “, PA”. The package
must be able to update existing rows (just the description, as the table only contains two columns, the county ID and description).

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Look for “County” in description & split
descriptions having “County”.

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Parse description field & build new description.

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Union all transformed & existing description rows.

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• Timesheet Table Spec: Take the several CSV files, EmpTime####.CSV, from the folder C:TIME, and create a package that will read the
contents of the CSV files and add them to the SQL Server AllWorks OLTP database. The package must be able to insert rows that don’t exist in the
table, based on the combination of the Job Number ID, Employee ID, and Date. An employee can work on multiple Jobs in a single day, but there
will never be more than one row per Job/employee/date. The package must validate that each Job Number ID exists in the project table and that
each Employee ID exists in the employee table. If not, the incoming record should be written to a flat file. The package should update existing
timesheet record (for changes to hours worked), based on the Job Number ID/Employee ID/Work Date. The package must validate that a
timesheet record cannot be posted for a Job that has been closed, if the work date exceeds the Job close date. If this occurs, the incoming
timesheet record should be written to a flat file.

No input files precedence
constraint expression.

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There are multiple timesheet input files each having a different name so the
ForEach Loop container must capture the input file name dynamically using the
Connection Manager named „Dynamic Input Time Sheet File‟ .

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The package is unlike the other packages, because a variable # of CSV files
have to be read through. Therefore, two sets of variables need to be maintained
– a count of rows added/inserted/errors for the file, and then a count of rows
added/inserted/errors for the folder – using a Script Task.

public void Main()
{
// TODO: Add your code here
Dts.Variables["FilesProcessedCount"].Value = (int)Dts.Variables["FilesProcessedCount"].Value + (int)1;
Dts.Variables["FolderAddedCount"].Value = (int)Dts.Variables["FolderAddedCount"].Value +
(int)Dts.Variables["NewCount"].Value;
Dts.Variables["FolderChangedCount"].Value = (int)Dts.Variables["FolderChangedCount"].Value +
(int)Dts.Variables["ChangedCount"].Value;
Dts.Variables["FolderInvalidCount"].Value = (int)Dts.Variables["FolderInvalidCount"].Value +
(int)Dts.Variables["InvalidCount"].Value;
Dts.TaskResult = (int)ScriptResults.Success;
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}

The Process
Timesheet
Data Flow
task.

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• Material Purchase Table Spec: Take the XML file, MaterialPurchases.XML, from the folder C:, and create a package that will read the
contents of the XML file and add them to the SQL Server AllWorks OLTP database. The package must be able to insert missing rows based on the
ID. The package should update existing material purchase records, based on the ID. The package must validate that each Project ID and Material
Type exists in the Project and Material Type tables. If not, the incoming record should be written to a text file. The package must validate that a
material purchase record cannot be posted for a project that has been closed, if the purchase date exceeds the project close date. If this occurs,
the incoming material purchase record should be written to an error file.

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The SalePoint input
field is checked
for null values &
converted to an empty
string if nulls are found.

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• Invoice Table Spec: Take the Excel spreadsheet. Invoices.XLSX, from the folder C:, and create a package that will read the contents of the
file and add them to the SQL Server AllWorks OLTP database. The package must be able to insert missing invoice based on the Invoice ID, which
uniquely identify a row. The package should update existing invoice records, based on the Invoice ID. The package must validate that the Project
ID exists. If the Project ID does not exist, the package should write the incoming row to a flat file.

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The Terms & Work Date
input fields are
checked for null values
& converted to an empty
string if nulls are found.

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• Master Package: Create a master package that will include all the ETL packages using execute package tasks to execute ETL packages from
the SQL Server. Set up package variables and set ETL package configurations to parent/child. Deploy all ETL packages to SQL Server and keep
these packages grouped by using a sequence container and set precedence constraints to mirror PK/FK database constraints & join the packages
depending on their dependencies within a sequence container. Use a SQL Agent Job in SSMS to schedule the Master package only. Outside of
the sequence container use maintenance tasks to do the following: copy data files from the server to C: folder; backup database; shrink
database; rebuild index; update database statistics; create one send email failure connected to each of the database tasks and one successful
after last database task. Run the Master package on the SQL Server & deploy to SQL Server. Create a job in SQL Server using SQL Agent.

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The Sequence
Container flow
continued.

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The SQL
Server Agent
job properties.

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The SQL Server Agent
job properties to
schedule & execute the
master package.

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• Introduction: An effective data warehouse is designed to facilitate the analysis and reporting of this
stored data repository. With this, it is imperative to provide a fundamental knowledge of data warehousing
and database programming concepts as well as Kimball data modeling concepts in building a
multidimensional database design. Some of the dimensional modeling topics covered included: study of
the Ralph Kimball methodology (and compare to the Bill Inmon methodology for data warehousing); study
differences between OLTP, Data Warehouse, and OLAP databases; study OLAP storage methodologies/
persistence models (MOLAP, HOLAP, ROLAP); and discussed uses of surrogate keys. The SSAS session of the
course included: analysis of many different dimension and fact table patterns, across different industries
included: Star and Snowflake schemas; Slowly Changing Dimensions; Role Playing dimensions; Many to many
relationships; Bridge tables; Dimension hierarchies, multiple hierarchies and Dimension attributes; Common
business dimensions (product, date, account/customer); Dimension outriggers; Multiple fact tables at
different levels of dimension granularity; General patterns and practices for loading dimension tables and
fact tables; Degenerate Dimensions; Factless Fact Tables; Periodic Snapshot fact Tables; Junk Dimensions;
and Fully-additive and semi-additive facts.
• The SQL Server Analysis Services (SSAS) BI tool provides the ability to create online analytical processing
(OLAP) data structures (cubes) that allow fast analysis of data. Some of the SSAS topics covered in the
course included: the basics of using SSAS to build OLAP cubes; applying the different dimension models
from the Data Warehousing/Dimension Modeling exercises to create actual OLAP databases; the SSAS Cube
Editor; the SSAS Dimension Editor; MDX Calculations and Analysis Services KPIs; drillthrough report actions;
creating OLAP perspectives; OLAP storage methodologies (MOLAP, HOLAP, ROLAP); creating OLAP partitions
and OLAP aggregations ; integrating SSAS with SSIS; XMLA to manage an OLAP environment OLAP Security
Roles; SSAS Data Mining; and backing up and restoring OLAP databases.
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• Overview/Specifications: Building off of the SSIS project, students create a data warehouse and build
some analysis package solutions using Analysis Services, setting the data warehouse as the data source.
Students denormalize the OLTP database, create fact and dimensions tables (including Drop and Create
Stored Procedure scripts for the tables and PK/FK constraints), and set up an OLAP database containing one
cube with multiple dimensions (one of the dimensions will be used in the cube twice as a role-playing
dimension). Students also write MDX queries (10 calculations and 10 named sets for key performance
indicators (KPIs)), create 4 KPIs and display the results in Excel to analyze and measure AllWorks profitability
and costs. All Cost and Profit measures as Currency and Percent measures will be formatted as ‘Percent’.
Hierarchies are created in Date, Project, and Employee dimensions based on the key columns. All dimension
attributes and hierarchies will be built so that anyone retrieving data through a cube browser interface will
see descriptions, not keys, except for the EmployeeKey (which is renamed to 'ID'). Partitions are created for
each of the fact tables with an aggregation design for a 50% performance increase. Four perspectives will be
created. A role will be created granting Authenticated Users Read Definition access to the OLAP database
and Read access to the cube. A URL action will be created that pulls up a GoogleMap for the Counties.

• A separate MDX workshop solution is provided along with the SSAS project. MDX is the programming/
querying language for OLAP databases. Topics for the 19 MDX queries include: general MDX query syntax;
MDX context; hierarchical functions; date-based functions (ParallelPeriod, PeriodsToDate, LastPeriods, etc.);
Permanent Calculation Members and Named Sets; Ranking and TopN retrieval; aggregation functions; and
KPI Programming. Calculations and sets are created at the query scope, not the session or cube scope.

• This project was completed by each student individually. Specifications and solutions are provided on the
following slides. Note that the dimensional model was provided for the students.
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These are the table specifications provided from the physical data model:

Below are the SQL DDL Scripts that contain code to drop, create, & load the fact & dimension
tables. The scripts to build & load the dimension tables will be run first. The scripts to create all the
fact tables will be run second.

This script to create & load the DimDate dimension table must be run first, prior to any
other script execution since some of the other scripts join to the DimDate table:

The next step is to create the remaining dimension tables. These scripts to create all the other
dimension tables can be run in any order:

The script to create & load the FactLabor fact table must be run first before any other
fact table script execution since some of the other scripts join to the FactLabor table:

The final step is to create & load the remaining fact tables which can be run in any order:

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This is the AllWorks data warehouse. It
contains the data staging area relational
database built by the SSIS project ETL
process from the flat file operational source
system. It also contains the data presentation
area built from the scripts in the previous slide.

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This is the AllWorks OLAP
solution. Also shown here
is the data source view for
the star schema model.

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The AllWorks
OLAP cube is
built from the
data source view
using the cube
wizard. The
Cube Designer
shows the fact &
dimension tables
and provides the
ability to refine
the cube.

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Dimension Designer is used to
create a hierarchy for the Dim
Date dimension. The Attribute
Relationships tab is used to
define and refine a natural
hierarchy to optimize cube
performance. The Name
Column property is changed to
display the name column.

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Dimension Designer is used
to create a hierarchy for the
Dim Project dimension. The
Attribute Relationships tab is
used to define and refine a
reporting hierarchy to
optimize cube performance.

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Dimension Designer is used
to create a hierarchy for the
Dim Employee dimension.
The Attribute Relationships
tab is used to define and
refine a reporting hierarchy to
optimize cube performance.

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Dimension Usage
shows the
relationships (type
and granularity)
between the fact
and dimension
tables. As per the
specs a Job
Closed Date role-
playing dimension
is added as a
referenced
(snowflake)
dimension through
the Dim Project
dimension and the
Dim Date
dimension.

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Several Calculated
Members are
created to be used
in KPIs and MDX
queries. This
member divides the
invoice amount
using another
calculated member
for a percentage
when the invoice
amount is > 0. The
format is displayed
as „Percent‟.

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This member
uses the Parallel
Period MDX
function to
retrieve the count
for the previous
quarter.

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Several
Named Sets
are created to
be used in
KPIs and
MDX queries.
This Named
Set is used to
retrieve the
client children
only.

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This Named
Set is used to
filter the
overhead cost
that are > 0 for
the prior
quarter
children only
using the
ParallelPeriod
MDX function.

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Four KPIs are
created to be used
in Excel & show
the status of
important business
metrics. Here are
the specs:

Shown here is the
solution for KPI2.
The Value
Expression is a
Calculated
Member. Here are
the Excel
solutions:

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This Standard URL
Action allows the
user to right-click on
a measure value in
an Excel cell and
display this „County
GoogleMap‟ in the
Additional Actions
submenu. Clicking
on this action in
Excel will launch a
web Google Map for
the county value
associated with the
measure.

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Physical storage
partitions are
created for each of
the four fact tables
– one for data on or
after June 16, 2005
and one for data
before that date.
This will provide an
active partition for
the most recent full
years of data, and
then store all older
data in an archived
partition.
Performance will
increase for this
default MOLAP
storage on queries
that use “recent”
data. Partitions will
be optimized using
Aggregations.

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The Aggregation
Design Wizard is
used to optimize
the partition‟s
aggregations for
a 50%
performance
increase.

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Perspectives are
created to
provide subsets
of the cube which
simplifies the
view of the cube
for improved
usability to end
users. Particular
measures are
selected for each
perspective.

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Besides measures,
these perspectives
contain other
objects like KPIs,
measure groups,
dimensions,
hierarchies, and
attributes.

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Perspectives also
contain actions,
named sets, and
calculated
members.

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A security role is
created to assign
„read‟ permission
(cannot modify)
to database
objects and
properties for
user access.

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„Read‟ security
access is granted
to the
„Authenticated
Users‟ Windows
group for the
„AllWorks‟ cube in
the „AllWorksDW„
OLAP database.

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„Read‟ security
access is granted
to all dimensions
on this subtab
which controls
access directly to
a dimension from
a front-end
application.

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This is the start of
the MDX Query
Workshop. Here
are the specs for
all queries:

Each query
solution and it‟s
result set are
shown on this and
the following
slides. They will
use calculated
members and
names sets
created in the
cube and the
corresponding
cube perspective.

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The query
WHERE clause
is only for
dimension slicing
& defining. It
gets evaluated
first. The query
is restricted to
using one
measure in the
WHERE clause.

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The last 52
periods from the
current member
are used to
calculate a
moving average.
Lastchild is used
to display only
the last 52
periods.

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The use of an
asterisk signifies
a cross join. Use
of commas is an
implied cross join
which is the
same thing as an
asterisk.

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The Order
function arranges
members of the
specified set
according to their
position in the
hierarchy, and
then descending
purchase amount
order.

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Using Member
returns the set of
members in a
dimension and
returns a total for
all members in a
set. Using Order
sorts jobs in
order of purchase
amount and an
asterisk shows
the breakdown in
each job by
material type.

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Break hierarchy
descending is
used to break the
outer left parent
in the join to
order labor rates
highest to lowest.

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„With Member‟
defines a
calculated
member which is
helpful when the
member is used
more than once
in a query as
shown here to
get the labor rate.

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Generate is used
to apply each job
set member to
each member of
the employee set,
then joins the
resulting sets by
union. The
asterisk is used
to display the job
and employee.

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Parallelperiod is
used to return the
quarter member
from 4 prior
quarter periods
ago. The positive
number „4‟
assumes going
back 4 quarters.

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Topcount sorts
the jobs labor
cost set in
descending order
and returns 10
elements with the
highest values
to get the top 10
projects.

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The Children
function returns a
naturally ordered
set that contains
the children of
the specified
„Employee
Name‟ member.
Using „Children‟
with a hierarchy
requires a base
point.

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The Format
String cell
property applies
a format
expression
against the value
to return a
formatted value.
The „0‟, „.‟, & „%‟
symbols are
placeholders.
The „#‟ symbol is
a placeholder
only if a value
exists.

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Filter is used to
restrict output by
evaluating the
specified logical
expression
against the
Employee Name
set. It requires 2
parameters: the
set you‟re filtering
on & the and the
filter condition
expression
(where Carl
Imhoff incurred
labor costs).

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Non empty is
used to filter out
null rows from a
specified set. All
columns in the
row-set must be
empty for this to
work.

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This searched
Case statement
evaluates a set of
Boolean
expressions (total
profits = 0) to
return specific
values (null) if
true. It is used
here to avoid
division by 0 in
the cube
calculated
member.

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MDX requires
enclosing sets in
curly braces { }.
When using
multiple
measures, they
must go in curly
braces { } as a
set. Since a
Named Set is
being used here,
it is not required
to enclose the
output of the
function in curly
braces.

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The „Having‟
clause is a way of
filtering the final
result set. Here
we are retrieving
only clients with
the word „Inc‟ in
the client name.

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The TopPercent
function
calculates the
sum of the
specified numeric
expression (30)
evaluated over
the invoice
amount for all
jobs set, sorting
the set in
descending order
and returns the
elements with the
highest values
whose summed
value is ≥ 30.

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• Introduction: Many high performing companies are becoming more analytical by turning to Business
Intelligence (BI) capabilities, BI reporting, and data that is integrated and analyzed across the business for
examination to help them get a better understanding of the key performance indicators that drive their
business. BI reporting enables users to make decisions that affect how an organization is run. To be useful,
BI reporting must turn dynamic, detailed data into information and make it available in real-time, at the
point of a decision. It must be accessible on-demand when and where it’s needed and deliver optimal
performance for data loading, ad hoc queries, and complex analytics. The Microsoft Business Intelligence
stack provides the tools that allows for this analysis while incorporating OLAP capabilities for quick reporting
performance. The SQL Server Reporting Services (SSRS) BI tool is a full-featured report writer and rendering
engine (for multiple formats) used to create, execute, and deliver reports, tables, and charts that summarize
and illustrate the data stored in a variety of data sources. SSRS is web enabled. The Performance Point
Server 2007 (PPS) BI tool is for performance management using dashboards, scorecards, reports, charts, and
KPIs. The venerable Excel 2007 product has been enhanced with cube/analysis capabilities and delivers to
an end-user’s Excel spreadsheet data that can be used for planning, analytical, and reporting purposes, from
an SSAS OLAP cube, and adds all-important spreadsheet computational, formatting, and graphical features.
Most importantly, since the interface is Excel, users enjoy the full familiarity and write-back capabilities of
their spreadsheet for planning, budgeting, and forecasting tasks. SharePoint Server 2007 enterprise
information portal can be configured as a web-based intranet that helps simplify BI and can help improve an
organization's effectiveness by streamlining the management of and access to data by providing data
integration, data crawling, and report design to enable business decision making. SharePoint's multi-purpose
platform allows for BI tooling through using the collaboration features of SharePoint with reports and
integrating with SSRS to provide a complete business intelligence platform.

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• Overview: The SSRS session of the course focused on SSRS, PPS, Excel, and SharePoint (concentrating on BI
with SharePoint and Office Excel Services). There was some coverage of SharePoint administration but not as
heavy as a SharePoint for Developers class. The course topics for each subject are as follows: SSRS: Learn
the basics of the SSRS report writer; Learn how to create reports with groups, parameters, subtotals, running
totals, calculations, etc.; Learn how to create matrix (pivot table) reports; Learn how to leverage the latest
charting enhancements in SSRS 2008; Learn SSRS Expressions; Learn how to incorporate SQL queries and
stored procedures into reports; Learn how to schedule reports for automatic execution and deliver through
report subscriptions; Learn the basics of integrating SSRS with .NET; Use subreports for more advanced
output formats; Reporting against either relational or OLAP databases. PPS: Creating KPI scorecards and
analytic charts/grids; Creating Dashboard pages and dashboard filters; Create KPI scorecard hotlinks to
related charts/grids; Incorporating content from Excel and SSRS; Publishing dashboards to SharePoint.
SharePoint: Creating Business Intelligence Site Collections; Configuring SharePoint to receive Excel content
(Trusted site locations); Configuring a site collection to store document libraries for PPS, SSRS, and Excel
Content; Scheduling SSRS reports for automatic deliver to SharePoint SSRS document libraries. Excel
Services: Creating Pivot Tables and Pivot Charts against OLAP data; Publishing Excel content to a SharePoint
document library.

• The SSRS project is combined with an Excel/PPS/SharePoint Project using the concepts described above.
Students design and build each reporting component using the SSAS project OLAP database solution, then
deploy to SharePoint. Here are the general specs: This project was completed by each student
individually. Each report specification and solution are provided on the following slides.

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This is the entire
SharePoint Site
collection solution.
Note the Document
Libraries created as
per #1 in the specs.

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The data sources
are uploaded for the
entire SharePoint
Site collection
solution as per Part
One of the specs.

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This is the entire
PerformancePoint
Server solution.
Details of each
component are on
the following slides.

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These are the KPIs
used for the entire
PPS solution (see
indicators used on
the next slide).

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These are the KPI
indicators used for
the entire PPS
solution.

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These are the 2
scorecards for page
1 of the dashboard.

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These are the
reports used for the
entire PPS solution.

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First, the Data
Sources are set up
as per the specs.

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The 1st page in the
AllWorksDashboard
consists of two
scorecards and the
name of the page is
simply called
„Scorecards‟. Here
are the specs:

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The Overhead
Scorecard is
created for the left
side of the
dashboard window.
As per the specs,
the scorecard uses
the Overhead
perspective and the
Overhead Trend
KPI. Also per the
specs in the Editor
tab, the
OverheadTotal and
AllOverheads
named sets from
the cube are placed
below the KPI to
show totals.

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As per the specs,
only the Overhead
Scorecard uses the
Overhead Quarters
filter from the cube.
The filter is linked to
the Overhead
Scorecard.

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The KPIs are
imported from the
cube. The value is
reformatted to show
as a percentage.

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The Client Job
Financials
scorecard is created
for the right side of
the dashboard
window. As per the
specs, the
scorecard uses the
Job Summary
perspective, two
objective KPIs, and
multiple cube KPIs.

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The 1st objective
KPI is „Client
Financials‟. This
KPI is dropped to
the left of the other
KPIs in the Editor
tab of the
scorecard.

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Included below the
Client Financials
KPI is the „Open
Receivables as a %
of Invoiced‟ KPI.
This KPI is imported
from the cube and
has its value
reformatted to show
as a percentage.
As per the specs in
the scorecard Editor
tab, the ClientTotal
and
ClientsOpenReceiv
ables named sets
from the cube are
placed below the
KPI to show totals.

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Also included below
the Client Financials
KPI is the „Profit %‟
KPI. This KPI is
imported from the
cube and has its
value reformatted
to show as a
percentage. As per
the specs in the
scorecard Editor
tab, the ClientTotal
and AllClients
named sets from
the cube are placed
below the KPI to
show totals.

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The 2nd objective
KPI is „Construction
Job Financials‟.
This KPI is dragged
underneath the
„Client Financials‟
KPI in the Editor tab
of the scorecard.

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Also included below
the „Construction
Job Financials‟ KPI
is the „Overhead as
% of Total Cost‟ KPI
imported from the
cube. As per the
specs in the
scorecard Editor
tab, the JobTotal
and AllJobs named
sets from the cube
are placed below
the KPI to show
totals.

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The 2nd page in the
AllWorksDashboard
is a bar chart report
and the name of
the page is simply
called „Materials‟. It
uses the
ClientsPlusAll named
set as the filter. Here
are the specs:

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This analytical
chart report for
page 2 uses
the material
perspective
data source
with a single
client.

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The 3rd page in the
AllWorksDashboard
is a line chart report
and the name of
the page is simply
called „Overhead‟.
It uses the
AllOverheads named
set as the filter. Here
are the specs:

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This analytical
chart report for
page 3 uses
the overhead
perspective
data source for
multiple
overhead
categories.

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The PPS reports
are deployed to
the
AllWorksDashbo
ard of the
PPSDashboards
document library
in SharePoint.

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These are all the
PPS reports in
the SharePoint
PPSDashboards
document library.

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report
(Scorecards) of
PPSDashboards
in SharePoint.

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report
(Materials) of
PPSDashboards
in SharePoint.

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report
(Overhead) of
PPSDashboards
in SharePoint.

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Created a team
SharePoint site
for collaboration.

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