Dataflow is the initial Data Preparation that takes place in Power BI for the report, to begin with. Power BI follows an ETL-Extract, Transform and Load process to perform the function. Power BI now brings the flexibility of ETL to be self-service through simple interface/navigation. Dataflows creation is performed inside the Power Query functionality.
Image 1: Structure in Power BI Data and Reporting
Data Flows can be easily be created by performing the below steps:
1. Navigate to your workspace and select on Dataflow
2. Go to the +Createon the top right to bring in a dropdown
3. Select the option –> Dataflow
Image 2: Data Flows Creation
The below mentioned 3 options will be visible inside the Dataflow creation:
1. Entities
2. Linked Entities
3. Common Data Model
1. Entities
An entity is a set of fields that are used to store data, much like a table within a database.
Image 3: Choose an option
Select an appropriate entity to start the dataflow creation. The user could see a simpler and rich UI design screen, helping us to choose the data source connection we need. This is applicable for cloud, on-premise or even a simple excel sheet.
Image 4: Data Sources in Power Query
Now, select the data source that you need to connect to your data.
Image 5: Connectivity gateway for the data source
Choose the appropriate tables inside to fetch the data from.
Image 6: Tables in Data Source
Once the tables are selected, we can then proceed to use the dataflow editor – ETL. This step is very similar to the initial power query we have for cleansing our data in Power BI desktop but hosts much more advanced functionalities to cleanse, refresh and schedule your data.
Once you’ve created a dataflow, you can define its ‘refresh frequency’ by setting a refresh schedule in the dataflow settings. You can also use the Advanced Editor for defining your queries in M language.
2. Linked Entities
Linked entities allow users to reuse data which exists in the lake, thereby allowing them to manage and organize projects with complex ETL processes, from one or multiple sources, while also letting analysts build on each other’s work.
3. Common Data Model
These are Microsoft Standardized Schemas for your data. Once we have finished our cleansing process we can start with the transformation mapping fields process and leverage the use of a common data model. To leverage the Common Data Model with your dataflow, click on the ‘Map to Standard’ transformation in the ‘Edit Queries’ dialog.
Image 7: Mapping Fields
If any fields do not get mapped to the common data model fields they are pushed to be null. You can then proceed to save your dataflows. Finalize your dataflows and create ‘scheduled refresh’ for your data.
Image 8: Refresh Scheduling in Power BI
Now you can consume the dataflows directly in Power BI Desktop and use them for your reporting and analysis.
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