There are two main kinds of database schema:
- A logical database schema conveys the logical constraints that apply to the stored data. It may define integrity constraints, views, and tables.
- A physical database schema lays out how data is stored physically on a storage system in terms of files and indices.
The "Data Warehouse Views" feature is a method of creating new warehoused tables by modifying an existing table, or joining or consolidating multiple tables together through the use of SQL.
ROLAP stands for Relational OLAP, an application based on relational DBMSs. MOLAP stands for Multidimensional OLAP, an application based on multidimensional DBMSs.
OLAP data is typically stored in a star schema or snowflake schema in a relational data warehouse or in a special-purpose data management system. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.
SQL Server Analysis Services (SSAS) is the technology from the Microsoft Business Intelligence stack, to develop Online Analytical Processing (OLAP) solutions. In simple terms, you can use SSAS to create cubes using data from data marts / data warehouse for deeper and faster data analysis.
SSIS, SSAS, SSRS are tool set by SQL server to develop data warehouse and BI solutions. SSIS is the SQL server tool for ETL. SSRS is reporting and visualisation tool for SQL Server. Using SSRS one can create, manage and publish reports and dashboards.
The Ship Security Alert System (SSAS) is a safety measure for strengthening ship's security and subduing acts of piracy and/or terrorism against shipping.
SSAS is Microsoft SQL Server's Analysis Services which is an online analytical processing (OLAP), data mining and reporting tool used in Business Intelligence to make your data work for you. SSIS stands for Sql Server Integration Services. The key power of SSIS is its data transformation and migration capability.
To Connect Directly to an Analysis Services Database by using SQL Server Data Tools
- Open SQL Server Data Tools.
- On the File menu, point to Open and then click Analysis Services Database.
- Select Connect to existing database.
- Specify the server name and the database name.
- Click OK.
You can think of the tabular model as a cross between a SQL Server relational database and an SSAS multidimensional cube. Like a database, the tabular model supports tables and their relationships. Like a cube, the model also supports measures and key performance indicators (KPIs).
As I said in a post before, SSAS multidimensional is dead! SSIS though doesn't have the same problem as SSAS Multidimensional had when Tabular appeared. There isn't a “Special SSIS” that Microsoft can push to users to replace the current experience. There is, nevertheless, the cloud experience.
SSAS Database BackupRight-click on the SSAS database in the object explorer window of SSMS, and select Back Up. This should bring up a window as shown in the below screenshot. Provide the backup file name, and select the options for compression as well as encryption.
online analytical processing
This tool is an extremely powerful tool to create analytical reports fast. It works with cubes that are designed to generate reports with optimal performance using aggregated information stored in the cube. SSAS generally does consume a lot of space because it contains a lot of information ready to be used.
- Open Microsoft Excel and select Data | pivot grid and PivotChart Report to open the wizard.
- Select External Data Source in Step 1 of the wizard.
- Click Get Data in Step 2 of the wizard.
- Select the OLAP Cubes tab in the Choose Data Source window and locate your OLAP cube file.
- Complete the wizard.
Businesses can use OLAP cubes for a range of activities, including budgeting, planning, analysis, and reporting. All information reporting and calculations remain consistent in an OLAP cube, and it allows for the quick creation and analysis of “what if” scenarios that help with forecasting.
The Tabular model reads data directly from the cache and takes advantage of the query acceleration resulting from the column store indexes, while the Multidimensional model is reading pre-aggregated data or atomic data from disk depending on the existing aggregations defined during the design of the cube.
Cubes are data processing units composed of fact tables and dimensions from the data warehouse. They provide multidimensional views of data, querying and analytical capabilities to clients. A cube can be stored on a single analysis server and then defined as a linked cube on other Analysis servers.