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Updating fact tables

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If you have identified information that needs data audit, create database tables as temporal system-versioned.

The following simple example illustrates a scenario with Employee information in hypothetical HR database: CREATE TABLE Employee ( [Employee ID] int NOT NULL PRIMARY KEY CLUSTERED , [Name] nvarchar(100) NOT NULL , [Position] varchar(100) NOT NULL , [Department] varchar(100) NOT NULL , [Address] nvarchar(1024) NOT NULL , [Annual Salary] decimal (10,2) NOT NULL , [Valid From] datetime2 (2) GENERATED ALWAYS AS ROW START , [Valid To] datetime2 (2) GENERATED ALWAYS AS ROW END , PERIOD FOR SYSTEM_TIME (Valid From, Valid To) ) WITH (SYSTEM_VERSIONING = ON (HISTORY_TABLE = dbo.

The capacity for an OLAP cube to store and organize colossal quantities of data is the attribute that makes OLAP more valuable for reporting than a standard relational database.

Queries that take 20 minutes to return results in a relational database can return the same results in less than 1 minute when the system uses previously calculated aggregate tables.

The one on the left is the original Nutrition Facts table and the one on the right is the new Nutrition Facts table.

You need to incrementally and automatically update the cubes.

This capability is critical to the successful implementation of an enterprise-level cube, yet it is one of the most under-documented and most easily misunderstood processes of OLAP Services.

So guys you have seen that we have successfully created all dimension datatables and 1 fact table.

Step 2 : In this step 2 we need to make relationship between fact-table and dimension-table.