How to transform one row into multiple columns in ADF? - azure

Question

I have TABLE as a source with just 1 row, like this in Azure data factory with 336 columns:
1
2
3
4
5
6
7
8
9
value1
value2
value3
value4
value5
value6
value7
value8
value9
And a want to combine every 3 columns into the first 3:
1
2
3
value1
value2
value3
value4
value5
value6
value7
value8
value9
What is the alternative to using Select on every 3 columns and then Join as it is long process with this many columns?

Answer 1

If your datasource is Azure SQL DB, you could conventional SQL to transform the row with a combination of UNVPIVOT, PIVOT and some of the ranking functions to help group the data. A simple example:
DROP TABLE IF EXISTS #tmp;
CREATE TABLE #tmp (
col1 VARCHAR(10),
col2 VARCHAR(10),
col3 VARCHAR(10),
col4 VARCHAR(10),
col5 VARCHAR(10),
col6 VARCHAR(10),
col7 VARCHAR(10),
col8 VARCHAR(10),
col9 VARCHAR(10)
);
INSERT INTO #tmp
VALUES ( 'value1', 'value2', 'value3', 'value4', 'value5', 'value6', 'value7', 'value8', 'value9' )
SELECT [1], [2], [0] AS [3]
FROM
(
SELECT
NTILE(3) OVER( ORDER BY ( SELECT NULL ) ) nt,
ROW_NUMBER() OVER( ORDER BY ( SELECT NULL ) ) % 3 groupNumber,
newCol
FROM #tmp
UNPIVOT ( newCol for sourceCol In ( col1, col2, col3, col4, col5, col6, col7, col8, col9 ) ) uvpt
) x
PIVOT ( MAX(newCol) For groupNumber In ( [1], [2], [0] ) ) pvt;
Tweak the NTILE value depending on the number of columns you have - it should be the total number of columns you have divided by 3. For example if you have 300 columns, the NTILE value should be 100, if you have 336 columns it should be 112. A bigger example with 336 columns is available here.
Present the data to Azure Data Factory (ADF) either as a view or use the Query option in the Copy activity for example.
My results:
If you are using Azure Synapse Analytics then another fun way to approach this would be using Synapse Notebooks. With just three lines of code, you can get the table from the dedicated SQL pool, unpivot all 336 columns using the stack function and write it back to the database. This simple example is in Scala:
val df = spark.read.synapsesql("someDb.dbo.pivotWorking")
val df2 = df.select( expr("stack(112, *)"))
// Write it back
df2.write.synapsesql("someDb.dbo.pivotWorking_after", Constants.INTERNAL)
I have to admire the simplicity of it.