The Varigence Blog
Every data solution benefits from a robust control framework for data logistics. One that manages if, how and when individual data logistics processes should be executed. A control framework also provides essential information to complete the audit trail of how data is processed through the system and is ultimately made available to users. BimlFlex provides the BimlCatalog for this.
In Azure, a Mapping Data Flow itself is not an object that can be executed directly. Instead, it needs to be called from an Execute Pipeline. This pipeline can be run, and in turn it will start the data flow. BimlFlex has advanced features to manage this.
BimlFlex output uses Parameters at Mapping Data Flow level to integrate with the BimlCatalog and store metadata for use inside the data flow. This post explains how to set this up in Biml.
When working with Biml in any situation, be it using BimlExpress, BimlStudio or BimlFlex, it can be helpful to peek into what is happening in the background. Biml provides logging features to do so.
Data Flow Mappings for Azure Data Factory allow a large degree of parametrization, which is important to strike a good balance between generating dynamic content and managing schema change.
BimlStudio can translate Biml into Data Flow Mappings, and this post looks into the deployment and results in Azure Data Factory.