<ul><li>Contents </li></ul><ul><li>SQL Server Integration Services (SSIS) </li></ul><ul><li>SQL Server Analysis Services (SSAS) </li></ul><ul><li>SQL Server Reporting Services (SSRS) </li></ul><ul><li>Performance Point Server (PPS) </li></ul><ul><li>SharePoint Server(SP) </li></ul><ul><li>MDX Programming </li></ul>Page 3 7 13 18 27 30 This portfolio contains examples of my development skills in the Business Intelligence arena. Contents
A new relational 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.
One SSIS package is created to do ETL for one target table. The following illustrates the data processing within 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.
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.
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.
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.
Large Scorecard with Multiple KPIs and their Hotlinks to a supporting report (Part 2 with partial Supporting Chart)
Large Scorecard with Multiple KPIs and their Hotlinks to a supporting report (Part 3 with the complete Supporting Chart)
This dual Y-axis chart created in PPS can be a great tool for data analysis as: 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 as in the report 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 as demonstrated below, allowing for the creation of additional charts (see chart in next slide) which permit one to investigate the contribution of various factors.
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.