This document contains a portfolio summary of the author's business intelligence projects using Microsoft technologies. It includes examples of SQL Server Integration Services (SSIS) packages to perform ETL on eight tables, SQL programming techniques, an SSAS cube developed on a star schema for an "All Works" database, deployment of reports to SharePoint server, and MDX queries used for analysis. An SSIS package sequence handles the ETL process and dependencies between tables to ensure data integrity.

1. Hong-Bing Li
Hongbingli88@gmail.com

2. Slide Contents
 3 SQL Server Integration Services (SSIS)
 8 SQL Programming
 15 SQL Server Analysis Services (SSAS)
 22 SharePoint Server
 25 MDX Programming
This portfolio contains examples of my business intelligence
projects using Microsoft BI Product Stack.

3. SQL Server Integration
Services (SSIS)
3

4. A new database “All Works” is setup as the staging area for the ETL process. A
thorough understanding of the relationships between the tables in the following
data diagram is important in determining the sequence of tables to be loaded and
in enforcing referential integrity.
4

5. A Script Task is utilized to maintain multiple sets of variables with scripts in C#, for
instance, one for keeping track of row counts of data processed dynamically at the
folder level, one for row counts at the file level.

6. One SSIS package is created to do ETL for one target table. The following illustrates
the data processing within the Job Timesheets package: the data process pipeline
starts by extracting data from a CSV file. The data is then conversed, processed and
transformed (filter, remove duplicates, lookups, validate) as it passes through the
pipeline, and is finally loaded into the target job timesheets table either as inserts
or updates. It logs any rows that error out for review and correction. Similarly,
seven more packages are generated for seven target tables.
6

7. 7
A Sequence Container is deployed to run the eight ETL packages in sequence based on the
relationships between the tables in the “All Works” Database to ensure referential integrity. If the
eight packages are processed successfully, data maintenance tasks are performed. A success or
failure notice email will be sent out depending on whether the data maintenance tasks are all
successfully completed or not.
A Master Package is created to contain the Sequence Container, the maintenance tasks and the
email notices; then a SQL Server Agent Job is setup to run the Master Package on a predefined
schedule to automate the entire data processing procedure.

8. SQL Programming
SQL Programming covers a broad range of content: Data Types and
Variables, Specialized Functions, Stored Procedures, Control-Of-Flow Operations,
Error Handling, Etc. Due to time/space constraints, I will illustrate only some of
the relatively new SQL functions/procedures/ programs that I have used in my
projects in the following:
1. Data Modification: OUTPUT clause, MERGE, select TOP(with ties) option
2. Aggregation: RANK() OVER (Partition by), GROUPING SETS
3. SQL Functions: PIVOT, CROSS APPLY
4. Common Table Expression (CTE), Correlated and Recursive Queries
5. User Defined Functions (UDF) that returns TABLE type
6. Error Handling Program:
Begin Try...End Try
Begin Catch...End Catch

15. SQL Server Analysis
Services (SSAS)
15

16. 16
The Development and Deployment of the All Works SSAS Cube
Data Source View of the Star Schema

17. 17
Browsing the All Works Cube Data

18. 18
Definition of Calculated Members

19. 19
Definition of Key Performance
Indicators (KPIs)

20. 20
This is an example of a KPI developed from the AllWorks.cube, which is deployed to
the Excel Spreadsheet for the end users. All the data in the cube, including all KPIs,
can be explored through the Pivot Table Field List.

21. Partitions Performed for the “All Works” Cube

22. SharePoint Server
22

23. Employee Labor Report
Deployed to a SharePoint Server

24. configure security settings: give users/
groups appropriate permissions.

25. MDX Programming
25

26. MDX query for the primary dashboard
(Slide 15 and 16 in SSRS Section)

27. This MDX Query shows two measures, Internet Sales Amount and
Internet Freight Cost, are sliced against three dimensions:
Date (FY Year), Product and Customer (Country)

28. ---The End---
Hong-Bing Li, February, 2015
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