STEP 1: CREATE A BUILD Select the target lifecycle after navigating to the Deploy page & click the “Make Current” button. The build is complete when the archive status is “current”. STEP 2: DOWNLOAD AND SAVE A BUILD Download the Archive.zip file to your target deployment server. Extract the contents & save in the location defined in the […]
The precedence solution makes executing & monitoring a LeapFrogBI project deployment a simple and fast task. To configure your project’s precedence and notification settings navigate to the precedence page in the deploy menu. Detailed instructions are included on each required input. Simply update your solution, save the settings, and your ready to build & deploy! […]
You select the grain (Year, Quarter, Month, Day, Hour, Minute), the start and end date, and whether or not to use a smart key as the surrogate. You can even use relative dates to help with situations where you want to grow the dimension on each execution. Finally, the LeapFrogBI d3000 template includes a set […]
LeapFrogBI generates packages which will create and maintain a data mart based onÂ component definitions.Â These packages must be run in order of precedence.Â This course describes the load precedence solution and demonstrates implementation.
After creating components, it is time to build and deploy.Â Configuration builds, component builds, and build archives are defined and initiated.Â Lifecycle migration along with a sample migration pattern is introduced.
Fact tables contain numeric measures and pointers to related dimensions. Fact components provide a simple interface for common tasks such as setting up dimension relationships, selecting degenerate dimensions, and creating calculations.
The attributes which describe related fact records are stored in dimension tables. It is the role of the dimension component to make creating and loading dimensions a very simple task. Set the dimension keys and selecting history tracking methods are a couple of common dimension tasks.
Pivot, unpivot, filter, union, join, aggregate, etcâ€¦ can all be done in transformation components. Dealing with data quality issues and structuring source records in a way that is appropriate for target dimensions & facts is the role of transform components.