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    <title>topic DataflowGen2 on MS Fabric: Optimize column profiling on millions of rows (1-5mil) in Dataflow</title>
    <link>https://community.fabric.microsoft.com/t5/Dataflow/DataflowGen2-on-MS-Fabric-Optimize-column-profiling-on-millions/m-p/3971235#M3089</link>
    <description>&lt;P&gt;Hello,&amp;nbsp;&lt;BR /&gt;&lt;BR /&gt;I have datasets residing on hive_metastore of Azure Databricks storage, in the range of 1-5 millions of records. I wish to use the Column Profiling feature on the entire dataset after loading the data using Get Data experience on a DataflowGen2 object .&amp;nbsp;&lt;BR /&gt;&lt;BR /&gt;When I am changing from "Column profiling based on top 1000 rows" to&amp;nbsp;"Column profiling based on entire dataset" , the processing takes forever when applied on the 1 million dataset, and happens instataneously when applied on the 1000 rows one.&amp;nbsp;&lt;BR /&gt;&lt;BR /&gt;&lt;EM&gt;&lt;STRONG&gt;What steps shall I take to optimize the performance here, to perform column profiling on entire data set?&lt;/STRONG&gt;&lt;/EM&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;/P&gt;&lt;P&gt;&lt;img /&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Tue, 04 Jun 2024 11:58:51 GMT</pubDate>
    <dc:creator>Anonymous</dc:creator>
    <dc:date>2024-06-04T11:58:51Z</dc:date>
    <item>
      <title>DataflowGen2 on MS Fabric: Optimize column profiling on millions of rows (1-5mil)</title>
      <link>https://community.fabric.microsoft.com/t5/Dataflow/DataflowGen2-on-MS-Fabric-Optimize-column-profiling-on-millions/m-p/3971235#M3089</link>
      <description>&lt;P&gt;Hello,&amp;nbsp;&lt;BR /&gt;&lt;BR /&gt;I have datasets residing on hive_metastore of Azure Databricks storage, in the range of 1-5 millions of records. I wish to use the Column Profiling feature on the entire dataset after loading the data using Get Data experience on a DataflowGen2 object .&amp;nbsp;&lt;BR /&gt;&lt;BR /&gt;When I am changing from "Column profiling based on top 1000 rows" to&amp;nbsp;"Column profiling based on entire dataset" , the processing takes forever when applied on the 1 million dataset, and happens instataneously when applied on the 1000 rows one.&amp;nbsp;&lt;BR /&gt;&lt;BR /&gt;&lt;EM&gt;&lt;STRONG&gt;What steps shall I take to optimize the performance here, to perform column profiling on entire data set?&lt;/STRONG&gt;&lt;/EM&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;/P&gt;&lt;P&gt;&lt;img /&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 04 Jun 2024 11:58:51 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Dataflow/DataflowGen2-on-MS-Fabric-Optimize-column-profiling-on-millions/m-p/3971235#M3089</guid>
      <dc:creator>Anonymous</dc:creator>
      <dc:date>2024-06-04T11:58:51Z</dc:date>
    </item>
    <item>
      <title>Re: DataflowGen2 on MS Fabric: Optimize column profiling on millions of rows (1-5mil)</title>
      <link>https://community.fabric.microsoft.com/t5/Dataflow/DataflowGen2-on-MS-Fabric-Optimize-column-profiling-on-millions/m-p/3972347#M3096</link>
      <description>&lt;P&gt;This is something that we don't have full control over. It relies on 3 specific components:&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;&lt;STRONG&gt;Data source:&lt;/STRONG&gt; and the resources available for it&lt;/LI&gt;
&lt;LI&gt;&lt;STRONG&gt;Connector:&lt;/STRONG&gt; owned by the Databricks team&lt;/LI&gt;
&lt;LI&gt;&lt;STRONG&gt;Power Query editor:&lt;/STRONG&gt; it effectively runs the queries that the connector tells it to run and it tries to later cache the results if they're fully computed&lt;/LI&gt;
&lt;/UL&gt;
&lt;P&gt;I can pass your feedback to the Databricks team, but there's nothing beyond what you're doing today that can impact the performance of the profiling for such scenario.&lt;/P&gt;</description>
      <pubDate>Tue, 04 Jun 2024 20:36:11 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Dataflow/DataflowGen2-on-MS-Fabric-Optimize-column-profiling-on-millions/m-p/3972347#M3096</guid>
      <dc:creator>miguel</dc:creator>
      <dc:date>2024-06-04T20:36:11Z</dc:date>
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