Business Intelligence: Multi-Dimensional Online Analytical Processing (MOLAP) with SQL Server Analysis Services Overview This purpose of this lab is to introduce various types of business intelligence tools and some of the common ways you can build out data visualizations such as dashboards, interactive reports and charts from data warehouse data. We’ll start with MOLAP development using SQL Server Analysis Services or SSAS. This will help you understand how the semantic model, where we add additional meaning to our data, helps support business intelligence. After you build your MOLAP Cube, you’ll visualize and explore the data with a variety of business intelligence tools to create interactive reports, charts, maps and dashboards. Disclaimer! As such it should be noted, this lab does not demonstrate everything you need to know about the MOLAP process, nor does it serve as training tool for SSAS, Excel, Power BI, etc. These activities are beyond the scope of this course. Yes, when you’re finished with the lab you’ll know your way around these tools, but more importantly you’ll understand the purpose and various capabilities of MOLAP databases, the semantic model, and how it connects to data visualization tooling. To complete this lab you will need the following: Access to your ist722_yournetid_dw database on SQL Server. Access to the SQL Server 2012 Data tools (used to create SSAS database objects). Microsoft Excel 2013. Microsoft Power BI Desktop. You should connect to your computer running your SQL server database before starting this lab. Part 1: Understanding Analysis Services Microsoft SQL Server Analysis Services 2014 allows us to build Multi-dimensional OLAP solutions. We can deploy these solutions to a special database which users can connect to and perform ad-hoc, interactive reporting. The OLAP database differs from a traditional database in that it supports: Rich metadata in our dimensions – for example we can sort by month number but display the month’s name so that the ordering of months are correct Jan, Feb, Mar, erc… Fixed and Ragged hierarchies Detection of measures in our fact tables, and their configuration as additive or semi-additive. Perspectives for limiting views into the OLAP database.
Business Intelligence: Multi-Dimensional Online Analytical Processing (MOLAP) with SQL Server Analysis Services Overview This purpose of this lab is to introduce various types of business intelligence tools and some of the common ways you can build out data visualizations such as dashboards, interactive reports and charts from data warehouse data. We’ll start with MOLAP development using SQL Server Analysis Services or SSAS. This will help you understand how the semantic model, where we add additional meaning to our data, helps support business intelligence. After you build your MOLAP Cube, you’ll visualize and explore the data with a variety of business intelligence tools to create interactive reports, charts, maps and dashboards. Disclaimer! As such it should be noted, this lab does not demonstrate everything you need to know about the MOLAP process, nor does it serve as training tool for SSAS, Excel, Power BI, etc. These activities are beyond the scope of this course. Yes, when you’re finished with the lab you’ll know your way around these tools, but more importantly you’ll understand the purpose and various capabilities of MOLAP databases, the semantic model, and how it connects to data visualization tooling. To complete this lab you will need the following: Access to your ist722_yournetid_dw database on SQL Server. Access to the SQL Server 2012 Data tools (used to create SSAS database objects). Microsoft Excel 2013. Microsoft Power BI Desktop. You should connect to your computer running your SQL server database before starting this lab. Part 1: Understanding Analysis Services Microsoft SQL Server Analysis Services 2014 allows us to build Multi-dimensional OLAP solutions. We can deploy these solutions to a special database which users can connect to and perform ad-hoc, interactive reporting. The OLAP database differs from a traditional database in that it supports: Rich metadata in our dimensions – for example we can sort by month number but display the month’s name so that the ordering of months are correct Jan, Feb, Mar, erc… Fixed and Ragged hierarchies Detection of measures in our fact tables, and their configuration as additive or semi-additive. Perspectives for limiting views into the OLAP database.