title: Read partioned hive table in pyspark instead of a parquet
body: <p>I have a partionned parquet. It is partioned by date like:</p>
  <pre><code>/server/my_dataset/dt=2021-08-02
  /server/my_dataset/dt=2021-08-01
  /server/my_dataset/dt=2021-07-31
  ...
  </code></pre>
  <p>The size is huge, so I do not want to read it at the time and I need only august part, therefore I use:</p>
  <pre class="lang-python prettyprint-override"><code>spark.read.parquet(&quot;/server/my_dataset/dt=2021-08*&quot;)
  </code></pre>
  <p>It works just fine. However I am forced to move from reading parquet directly to reading from the corresponding hive table. Something like:</p>
  <pre class="lang-python prettyprint-override"><code>spark.read.table(&quot;schema.my_dataset&quot;)
  </code></pre>
  <p>However I want to keep the same logic of reading only certain partitions of the data. Is there a way to do so?</p>