title: Spark 3.0 - Reading performance when saved using .save() or .saveAsTable()
body: <p>I'm wondering if there are differences in performance (when reading) between those two commands?:</p>
  <p><code>df.write.format('parquet').partitionBy(xx).save('/.../xx.parquet')</code>
  <code>df.write.format('parquet').partitionBy(xx).saveAsTable('...')</code></p>
  <p>I understand that for bucketing the question doesn't arise as it is only used with managed tables (saveAsTable()) ; however, I'm a bit confused regarding partitioning as to if there is a method to privilege.</p>