Hi, what is the recommended way to 'read' a subset of data from a Delta Table and either display the results in a notebook or use them in a DataFrame for further computation? Ideally, the chosen approach would utilize metadata statistics & partitioning (where available) to achieve optimal performance. a) spark.read.table("MyTable").filter("created_date > '2020-07-01'").display() b) spark.sql("select * from MyTable where created_date > '2020-07-01'").display() c) DeltaTable.forName(spark, "MyTable").toDF().filter("created_date > '2020-07-01'").display() Or does it not matter, and behind the scenes, they all result in the same processing logic being executed by Spark? Thank you

the core processing will be the same there might be some slight difference when reading the metadata/path but that should be negligible

Good to know. Thank you. Much appreciated.