Reports Dashboards Sql Developed by HongBing Li

1. Hong-Bing Li
Hongbingli88@gmail.com

2. Slide Contents
 3 Reports (SSRS)
 8 Dashboard: Scorecard & KPI (SharePoint)
 18 SQL Programming
 25 Data Integration Services (SSIS)
This portfolio contains examples of my business intelligence
projects using Microsoft BI Product Stack.

3. Reports (SSRS)
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4. 4

5. 5
Continued: Primary Dashboard

6. 6

7. 7
Whenever users make a selection on the "City" parameter, the cascading parameter "Product
SKU" is processed immediately. Its values are filtered dynamically based on two factors:
A. Selected cities B. Product SKU with dollar Sales greater than “0 “
The technique to implement cascading parameters in SSRS using MDX, which is based on OLAP,
is somewhat more complex than that using SQL, which is based on regular OLTP RDBMS.

8. Dashboard: Scorecard & KPI develope
(SharePoint)
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9. 9
Large Scorecard with Multiple KPIs and their Hotlinks to a
supporting report (Part 1). Right click a KPI, a supporting chart or table will
pop up to the right of the Scorecard, as shown in the next two slides.

10. 10
Large Scorecard with Multiple KPIs and their Hotlinks to a supporting
report (Part 2 with partial Supporting Chart)

11. Large Scorecard with Multiple KPIs and their Hotlinks to a supporting
report (Part 3 with the complete Supporting Chart)

12. This dual Y-axis chart is a great tool for data analysis: 1. Two different types of measures
can be analyzed simultaneously against dimensional data on the X-Axis, such as Dollar
Sales (left Y-axis) and Product Percent of Parent Sales (right Y-Axis) shown below; 2. These
two measures can be broken out further to provide more detail in tables or charts (see
below) where the right Y-Axis measuring Product Percent is further explained by the Product
Siblings breakout; 3. Data can be explored at different levels of the Hierarchy family (see
the top Product Hierarchy dropdown list) which functions as a filter, allowing one to obtain
summary and detail statistics at different levels accordingly and export them to Excel or
PowerPoint; and 4. Data points in the chart can be drilled down to various dimensions
allowing for the creation of additional charts (see chart in next slide) which permit one to
investigate the contribution of various factors.

13. Continued: this chart is generated by drilling down from the
previous slide. For example, the 21.32% of health and fitness
sales of parents in Aug. 2005 is broken out by region.

14. 14
Price Line-Chart

15. 15
Top 5 Cities with Monthly Sales Chart

16. Employee Labor Report
Deployed to a SharePoint Server

17. 17
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.

18. 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 Routine:
Begin Try...End Try
Begin Catch...End Catch

25. Data Integration
Services (SSIS)
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26. 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 data
diagram is important in determining the sequence of tables to be loaded and in
enforcing referential integrity.
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27. 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.

28. 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.
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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.

30. ---The End---
Hong-Bing Li
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