Hello! This is the eight video in a series of videos that will be posted on Azure Data Factory! Feel free to follow this series and other videos I post on YouTube! Remember to like, subscribe and encourage me to keep posting new videos!
- Azure Data Factory – Metadata Activity (Part 1)
- Azure Data Factory – Stored Procedure Activity (Part 2)
- Azure Data Factory – Lookup and If Condition activities (Part 3)
- Azure Data Factory – Foreach and Filter activities (Part 4)
- Azure Data Factory – Copy and Delete Activities (Part 5)
- Azure Data Factory – Web Activity / Executing a Logic App (Part 6)
- Azure Data Factory – Executing a Pipeline from Azure Logic Apps (Part 7)
Schema flexibility and late schema binding really separates Azure Data Factory from its’ on-prem rival SQL Server Integration Services (SSIS). This video focuses on leveraging the capability of flexible schemas and how rules can be defined to map changing column names to the sink.
Rule Based Mapping
Rule based mapping in ADF allows you to define rules where you can map columns that come into a data flow to a specific column. For example, you can map a column that has ‘date’ anywhere in the name to a column named ‘Order_Date’. This ability to define rules based allows for very flexible and reusable data flows, in the video below I walk through and explain how to set this up in side of a Select transform, enjoy!
This ( $$ ) Function in a Derived transform and a Select Transform
The this ($$) function simply returns the name of the column or value of the column depending on where it is used. In this video I show two use cases, one in a Select transform and one in a Derived transform.
If you like what you see and want more structured end to end training then check out the training offerings for Pragmatic Works! https://pragmaticworks.com/training