Posted by
Rayis Imayev
on
(2019-May-24)Data Flow as a data transformation engine has been introduced to the Microsoft Azure Data Factory (ADF) last year as a private feature preview. This privacy restriction has been lifted during the last Microsoft Build conference and Data Flow feature has become a public preview component of the ADF.
There are many different use-case scenarios that can be covered by Data Flows, considering that Data Flows in SQL Integration Service (SSIS) projects are still playing a big role to fulfill Extracting-Loading-Transforming (ETL) patterns for your data.
In this blog post, I will share my experience of populating a Data Vault repository by using Data Flows in Azure Data Factory.
First, I need to create my Data Vault model.
Data Model
For this exercise I've taken a date warehouse sample of AdventureWorksDW2017 SQL Server database, twhere I limited a set of entities to a small set of dimension tables (DimProduct, DimCustomer, DimGeography) and one fact table (FactInternateSales).
Fr…
There are many different use-case scenarios that can be covered by Data Flows, considering that Data Flows in SQL Integration Service (SSIS) projects are still playing a big role to fulfill Extracting-Loading-Transforming (ETL) patterns for your data.
In this blog post, I will share my experience of populating a Data Vault repository by using Data Flows in Azure Data Factory.
First, I need to create my Data Vault model.
Data Model
For this exercise I've taken a date warehouse sample of AdventureWorksDW2017 SQL Server database, twhere I limited a set of entities to a small set of dimension tables (DimProduct, DimCustomer, DimGeography) and one fact table (FactInternateSales).
Fr…
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