The Varigence Blog

In the real world, many data solutions require a high degree of flexibility so that they can cater to unique scenarios. This can be necessary because of specific systems that require integration, or simply because certain data needs bespoke logic to be interpreted correctly. BimlFlex offers ways to specify specific transformations at column level - using Custom Configurations.

With the 2021 BimlFlex release nearing completion, it's time to take a closer look at the patterns for Mapping Data Flows that will be made available in preview.

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.

BimlStudio can translate Biml into Data Flow Mappings, and this post looks into the deployment and results in Azure Data Factory.

In this first dev diary post we show the basic Biml syntax to create Data Flow Mappings for Azure Data Factory

In this development blog series, we explain how inline data sources and Delta Lake in particular will be supported in BimlFlex

Using BimlFlex to create an automated and integrated data warehouse