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Azure Data Factory Data Flow Transformations

Extend Transformation

Use the Extend Transformation to add your own custom data transformation via Spark coed that you're built, which can be included in ADF's Data Flow by providing a JAR file that Data Flow will execute for you on your Azure Databricks cluster.

Extend Transform

To add additioinal conditions, select "Add Stream" in the bottom configuration pane and click in the Expression Builder text box to build your expression.

Below is a Java example that applies "lower" to the "title" column using a Spark custom transformation. The first parameter and return type is required.


import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions;

public class ExtensionFrame {
	public static Dataset<Row> transform(Dataset<Row> df, SparkSession spark, String rowMarkerCol)  {
		return df.withColumn("shortTitle", functions.lower(df.apply("title")));

Method Field

In the transformation field "Method", enter "".

Upload JAR file

Author your custom transformation in either Java or Scala, build the JAR and then point to the JAR file in your Azure Blob Storage container. You can upload the JAR from your local file system using "Browse local".

External Dependencies

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