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    <title>topic Re: Spark DataFrame Checkpoint in Data Engineering</title>
    <link>https://community.databricks.com/t5/data-engineering/spark-dataframe-checkpoint/m-p/114366#M44790</link>
    <description>&lt;P&gt;Hello, yes this could help, although I would like to avoid mounting&lt;/P&gt;</description>
    <pubDate>Thu, 03 Apr 2025 06:31:05 GMT</pubDate>
    <dc:creator>NikosLoutas</dc:creator>
    <dc:date>2025-04-03T06:31:05Z</dc:date>
    <item>
      <title>Spark DataFrame Checkpoint</title>
      <link>https://community.databricks.com/t5/data-engineering/spark-dataframe-checkpoint/m-p/114263#M44766</link>
      <description>&lt;P&gt;&lt;SPAN&gt;Good morning,&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;I am having a difficulty when trying to checkpoint a PySpark DataFrame.&lt;BR /&gt;&lt;BR /&gt;The DataFrame is not involved in a DLT pipeline so I am using the &lt;STRONG&gt;df.checkpoint(eager=True)&lt;/STRONG&gt; command, to truncate the logical plan of df and materialize it as files within a Unity Catalog volume directory.&lt;/P&gt;&lt;P&gt;However, after some search, it seems that the checkpoint location needs to be an hdfs mounted directory.&lt;BR /&gt;I think this is deprecated in Unity Catalog and an alternative would be to write the df in the UC volume directory and then immediately read it back.&amp;nbsp;&lt;BR /&gt;&lt;BR /&gt;Does anyone know if hdfs is indeed deprecated in Unity Catalog and if the alternative mentioned above is a valid one ?&amp;nbsp;&lt;BR /&gt;&lt;BR /&gt;Thank you.&lt;/P&gt;</description>
      <pubDate>Wed, 02 Apr 2025 09:11:18 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/spark-dataframe-checkpoint/m-p/114263#M44766</guid>
      <dc:creator>NikosLoutas</dc:creator>
      <dc:date>2025-04-02T09:11:18Z</dc:date>
    </item>
    <item>
      <title>Re: Spark DataFrame Checkpoint</title>
      <link>https://community.databricks.com/t5/data-engineering/spark-dataframe-checkpoint/m-p/114286#M44775</link>
      <description>&lt;P&gt;how about mounting cloud storage?&lt;/P&gt;&lt;P&gt;spark.conf.set("spark.sql.streaming.checkpointLocation", "dbfs:/mnt/your-checkpoint-directory")&lt;/P&gt;</description>
      <pubDate>Wed, 02 Apr 2025 11:23:32 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/spark-dataframe-checkpoint/m-p/114286#M44775</guid>
      <dc:creator>saurabh18cs</dc:creator>
      <dc:date>2025-04-02T11:23:32Z</dc:date>
    </item>
    <item>
      <title>Re: Spark DataFrame Checkpoint</title>
      <link>https://community.databricks.com/t5/data-engineering/spark-dataframe-checkpoint/m-p/114287#M44776</link>
      <description>&lt;P&gt;your volume approach is also good idea&lt;/P&gt;</description>
      <pubDate>Wed, 02 Apr 2025 11:24:10 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/spark-dataframe-checkpoint/m-p/114287#M44776</guid>
      <dc:creator>saurabh18cs</dc:creator>
      <dc:date>2025-04-02T11:24:10Z</dc:date>
    </item>
    <item>
      <title>Re: Spark DataFrame Checkpoint</title>
      <link>https://community.databricks.com/t5/data-engineering/spark-dataframe-checkpoint/m-p/114366#M44790</link>
      <description>&lt;P&gt;Hello, yes this could help, although I would like to avoid mounting&lt;/P&gt;</description>
      <pubDate>Thu, 03 Apr 2025 06:31:05 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/spark-dataframe-checkpoint/m-p/114366#M44790</guid>
      <dc:creator>NikosLoutas</dc:creator>
      <dc:date>2025-04-03T06:31:05Z</dc:date>
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