DataFrame is simply a type alias of Dataset[Row] . These operations are also referred as “untyped transformations” in contrast to “typed transformations” that come with strongly typed Scala/Java Datasets.
The conversion from Dataset[Row] to Dataset[Person] is very simple in spark
val DFtoProcess = SQLContext.sql("SELECT * FROM peoples WHERE name="test"")
At this point, Spark converts your data into DataFrame = Dataset[Row], a collection of generic Row object, since it does not know the exact type.
// Create an Encoders for Java class (In my eg. Person is a JAVA class)
// For scala case class you can pass Person without .class reference
val personEncoder = Encoders.bean(Person.class)
val DStoProcess = DFtoProcess.as[Person](personEncoder)
Now, Spark converts the Dataset[Row] -> Dataset[Person] type-specific Scala / Java JVM object, as dictated by the class Person.
Please refer to below link provided by databricks for further details
https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html