In a DataFlow are partitioned Azure DataLake Gen2 Parquet files supported

[Anonymous]

I generated partitioned Azure DataLake Gen2 Parquet files using a spark notebook and can query them with no issues in Synapse Analytics using serverless SQL.

When I attempt to create a DataFlow using Azure DataLake Gen 2 storage using Url https://<my account>.dfs.core.windows.net/mothership/Transactions/SellingOrganizationId=3141

I see the 4 expected files.

However, when I press Combine I get

Am I doing something wrong or is this not supported.

Here is the POC spark notebook that generates the parquet file

using Microsoft.Spark.Sql;
using Microsoft.Spark.Sql.Types;

var df = spark.Read().Json("abfss://bdmtest@<myaccount>.dfs.core.windows.net/FLT/Trans/2011/04/30/**");

//get the schema of the data frame
var dfSchema = df.Schema() ;

foreach(var parentSchemaField in dfSchema.Fields) {
    
    if (parentSchemaField.DataType is StructType) {
        var childFrame = df.Select($"{parentSchemaField.Name}.*") ;
        foreach(var childSchemaField in childFrame.Schema().Fields) {           
            df = df.WithColumn($"{parentSchemaField.Name}.{childSchemaField.Name}",Col($"{parentSchemaField.Name}.{childSchemaField.Name}")) ;
        }
        df = df.Drop(parentSchemaField.Name) ;
    }
}



//create the computed year, month, and day columns for partitioning
df.CreateOrReplaceTempView("tmpTable") ;
var dfparquet = spark.Sql("SELECT *, SUBSTRING(CreateDate,1,4) AS Year, SUBSTRING(CreateDate,6,2) AS Month, SUBSTRING(CreateDate,9,2) AS Day FROM tmpTable") ;

dfparquet.Write().PartitionBy("SellingOrganizationId","Year","Month","Day").Mode(SaveMode.Overwrite).Parquet("abfss://mothership@<myaccount>.dfs.core.windows.net/Transactions") ;

  This works just fine you you use Power BI Desktop to load the data but you can only use the top most partition. in this case SellingOrganizationId=value  

[Anonymous · Accepted solution]

I am going to reply to my own question and hopefully it will close this issue and help someone else.

• What I needed was a Proof of Concept (POC) of putting data into a ADLS Gen2 storage account container where that container is partitioned by DataType/Year=2022/Month=01/Day=01.
• Then I wanted to create a DataFlow referencing that container to see if Power BI Desktop could use the entities in that DataFlow. 

Anyway the issue was that I could not get it to work in a dataflow and even if I did I doubt that Query Folding would work.

So the alternative was to use serverless sql in Azure Synapse.

• I could create multiple databases with each database targeting a container within ADLS Gen2.
• Rather than use AAD passthru I setup access to the CETAS table using a SAS token.
• Then I used TSQL to create a specific username/password for access to each database.
• Then I gave that username specific permissions.

This all worked fine until I discovered that you cannot refresh CETAS tables.

But it you do everything else the same and rather than create a CETAS table you create a view using OpenRowset then

"Bobs You Uncle".

Appending additional data to the underlying ADLS Gen 2 container as Year=xxxx/Month=xx/Day=xx works as expected.  I can see the refreshed data if I am attached using SSMS or if I  query in Azure Synapse.  I still have to refresh the DataFlow and for now until I can test using a Computed Engine I have to also refresh the DataSet but it does work in a DataFlow as expected.

Now I need to test using Lake Database tables to determine if they see appended data automatically.