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    <title>idea Silently process models on the local machine (option) in ArcGIS Pro Ideas</title>
    <link>https://community.esri.com/t5/arcgis-pro-ideas/silently-process-models-on-the-local-machine/idi-p/921324</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;Hello,&lt;BR /&gt;&lt;BR /&gt;Geoprocessing is generally performed faster in memory on the local machine or on a local hard drive as compared to over a network connection.&amp;nbsp; It would be useful if there was a geoprocessing option that will copy a models input data from the server, procecess the model on the local machine, then place the results back onto the server.&lt;BR /&gt;&lt;BR /&gt;Justification for this option:
&lt;UL&gt;&lt;LI&gt;Script Editors will not need to edit scripts to copy input data to the local machine so that we can take advantage of performance increases by processing data locally.&amp;nbsp; Instead we just click the option button.&lt;/LI&gt;&lt;LI&gt;Script Editors might not know what folders are avalible on the local machine - ie we cannot assume that C:\GISTemp is going to be on every machine.&lt;/LI&gt;&lt;LI&gt;This option will allow script editors to focus on the logical data flow on the server - the input and output datasets.&amp;nbsp; We will not have to edit scripts to temporary copy data to our local machine because the data processes faster on the local machine.&lt;/LI&gt;&lt;/UL&gt;
Example:&lt;BR /&gt;Inputs:&amp;nbsp; J:\InputData\EditingParcels.gdb,&amp;nbsp; J:\InputData\MyTaxdata.gdb&lt;BR /&gt;Outputs: J:\Public\Parcels.gdb
&lt;UL&gt;&lt;LI&gt;Join operations seem slow in ArcGIS when the data is from a network.&amp;nbsp; Having an option to "process data in memory / process data locally" will silently copy the input data to the local machine, run all of the models locally then push the outputs back to the server.&amp;nbsp; - And this can be done without needing to rewrite code.&lt;/LI&gt;&lt;/UL&gt;
Thanks&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&amp;nbsp;

&lt;!-- content transformation source ID: 087E0000000kBTG --&gt;

&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Wed, 03 Feb 2016 16:52:25 GMT</pubDate>
    <dc:creator>MarkVolz</dc:creator>
    <dc:date>2016-02-03T16:52:25Z</dc:date>
    <item>
      <title>Silently process models on the local machine (option)</title>
      <link>https://community.esri.com/t5/arcgis-pro-ideas/silently-process-models-on-the-local-machine/idi-p/921324</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;Hello,&lt;BR /&gt;&lt;BR /&gt;Geoprocessing is generally performed faster in memory on the local machine or on a local hard drive as compared to over a network connection.&amp;nbsp; It would be useful if there was a geoprocessing option that will copy a models input data from the server, procecess the model on the local machine, then place the results back onto the server.&lt;BR /&gt;&lt;BR /&gt;Justification for this option:
&lt;UL&gt;&lt;LI&gt;Script Editors will not need to edit scripts to copy input data to the local machine so that we can take advantage of performance increases by processing data locally.&amp;nbsp; Instead we just click the option button.&lt;/LI&gt;&lt;LI&gt;Script Editors might not know what folders are avalible on the local machine - ie we cannot assume that C:\GISTemp is going to be on every machine.&lt;/LI&gt;&lt;LI&gt;This option will allow script editors to focus on the logical data flow on the server - the input and output datasets.&amp;nbsp; We will not have to edit scripts to temporary copy data to our local machine because the data processes faster on the local machine.&lt;/LI&gt;&lt;/UL&gt;
Example:&lt;BR /&gt;Inputs:&amp;nbsp; J:\InputData\EditingParcels.gdb,&amp;nbsp; J:\InputData\MyTaxdata.gdb&lt;BR /&gt;Outputs: J:\Public\Parcels.gdb
&lt;UL&gt;&lt;LI&gt;Join operations seem slow in ArcGIS when the data is from a network.&amp;nbsp; Having an option to "process data in memory / process data locally" will silently copy the input data to the local machine, run all of the models locally then push the outputs back to the server.&amp;nbsp; - And this can be done without needing to rewrite code.&lt;/LI&gt;&lt;/UL&gt;
Thanks&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&amp;nbsp;

&lt;!-- content transformation source ID: 087E0000000kBTG --&gt;

&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 03 Feb 2016 16:52:25 GMT</pubDate>
      <guid>https://community.esri.com/t5/arcgis-pro-ideas/silently-process-models-on-the-local-machine/idi-p/921324</guid>
      <dc:creator>MarkVolz</dc:creator>
      <dc:date>2016-02-03T16:52:25Z</dc:date>
    </item>
    <item>
      <title>Re: Silently process models on the local machine (option) - Status changed to: Closed</title>
      <link>https://community.esri.com/t5/arcgis-pro-ideas/silently-process-models-on-the-local-machine/idc-p/1294269#M25153</link>
      <description>&lt;P&gt;You are correct, processing data stored on the local machine where ArcGIS Pro is installed will provide significantly better performance compared to processing remote data.&amp;nbsp;&lt;/P&gt;&lt;P&gt;However, given the lack of wide support for this idea, and other critical deliverables in the near term, this idea will be closed.&amp;nbsp;&lt;/P&gt;&lt;P&gt;You can use the Consolidate tools in the Data Management toolbox to get all data into a local folder for faster processing.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;A href="https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/an-overview-of-the-package-toolset.htm" target="_blank"&gt;https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/an-overview-of-the-package-toolset.htm&lt;/A&gt;&lt;/P&gt;</description>
      <pubDate>Tue, 30 May 2023 20:47:00 GMT</pubDate>
      <guid>https://community.esri.com/t5/arcgis-pro-ideas/silently-process-models-on-the-local-machine/idc-p/1294269#M25153</guid>
      <dc:creator>MargaretCrawford</dc:creator>
      <dc:date>2023-05-30T20:47:00Z</dc:date>
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