Managing Data Mart Changes
One of the most important advantages to LeapFrogBI’s metadata driven data mart development platform is the ability to adjust quickly to requirement changes. It has
Component Portability
LeapFrogBI account administrators configure project lifecycles in the project console. Each connection contains a definition for each lifecycle. After creating all of the required project
Use the Right Tool for the Job
Building a data mart can be broken down into three distinct steps. Extact data from source system Transform data to desired structure Load data into
Late Arriving Dimensions
When building a dimensional model it is critical that facts have accurate foreign keys pointing back to related dimensions. However, there are some situations where
Add Custom Scripts to Components
Users can now add custom scripts to components. For example, add indexes or update statistics using t-sql scripts. Built components will include the provided custom
Quick Review–ACID
One of the foundation concepts that all database workers should keep in the back (or maybe front) of their mind is ACID. This small and
Bulk Profile Upload
LeapFrogBI is a SaaS metadata driven ETL platform. At no time does LeapFrogBI connect directly to either the source or the destination systems. Therefore, the first
Fact Record Delete Component
The new f9001 component automates deletion of records from a fact table. Simply select the fact table to effect and a source to inner join
Stage Using REST API as Data Source
There is a wealth of data available via REST API much of which is publicly available. LeapFrogBI simplifies and automates the process of extracting data from
LeapFrogBI Success Story
I get asked on occasion to quantify the efficiency that the LeapFrogBI data warehouse automation platform offers. Below are a few measures that were collected