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    <title>question Re: Hive on Spark or Impala in batch Process (ETL) in Support Questions</title>
    <link>https://community.cloudera.com/t5/Support-Questions/Hive-on-Spark-or-Impala-in-batch-Process-ETL/m-p/300783#M220386</link>
    <description>&lt;P&gt;Hive is more adaptable as far as data arranges that it can check&lt;/P&gt;&lt;P&gt;- You may see Hive as more component wealthy as far as SQL language support and inherent capacities&lt;/P&gt;&lt;P&gt;- Hive will probably finish your inquiry regardless of whether there are hub disappointments (this makes it reasonable for long-running employments); this is valid for both Hive on MR and Hive on Spark&lt;/P&gt;&lt;P&gt;- If Impala can run your &lt;A href="https://www.sprinkledata.com/?utm_source=030820_SparkOrImpala&amp;amp;utm_medium=Cloudera" target="_self"&gt;ETL&lt;/A&gt;, at that point it will most likely be quicker&lt;/P&gt;&lt;P&gt;- Impala will come up short/prematurely end a question if a hub goes down during inquiry execution&lt;/P&gt;&lt;P&gt;- The last point may make Impala less reasonable for long-running occupations, obviously there is likewise a shorter disappointment window since questions are quicker, so Impala might just suit your ETL needs on the off chance that you can endure the faiure conduct&lt;/P&gt;</description>
    <pubDate>Mon, 03 Aug 2020 17:00:32 GMT</pubDate>
    <dc:creator>Henry2410</dc:creator>
    <dc:date>2020-08-03T17:00:32Z</dc:date>
    <item>
      <title>Hive on Spark or Impala in batch Process (ETL)</title>
      <link>https://community.cloudera.com/t5/Support-Questions/Hive-on-Spark-or-Impala-in-batch-Process-ETL/m-p/54314#M15579</link>
      <description>&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Hi All,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I have a doubt about performance and/or usable in batch process(ETL) between Impala or HoS.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I´ve read that impala is better in performance than HoS, but is not "best practice" (or not usual) to use in batch process (ETL).&lt;/P&gt;&lt;P&gt;Why? &lt;SPAN&gt;If it's the fastest&lt;/SPAN&gt;, why dont use at all?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;hugs,&lt;/P&gt;&lt;P&gt;Rodrigo Carvalho&lt;/P&gt;</description>
      <pubDate>Fri, 16 Sep 2022 11:32:52 GMT</pubDate>
      <guid>https://community.cloudera.com/t5/Support-Questions/Hive-on-Spark-or-Impala-in-batch-Process-ETL/m-p/54314#M15579</guid>
      <dc:creator>cetip</dc:creator>
      <dc:date>2022-09-16T11:32:52Z</dc:date>
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    <item>
      <title>Re: Hive on Spark or Impala in batch Process (ETL)</title>
      <link>https://community.cloudera.com/t5/Support-Questions/Hive-on-Spark-or-Impala-in-batch-Process-ETL/m-p/54377#M15580</link>
      <description>&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Some thoughts on your question:&lt;/P&gt;&lt;P&gt;- Hive is more flexible in terms of data formats that it can scan&lt;/P&gt;&lt;P&gt;- You may find Hive to be more feature rich in terms of SQL language support and built-in functions&lt;/P&gt;&lt;P&gt;- Hive will most likely complete your query even if there are node failures (this makes it suitable for long-running jobs); this is true for both Hive on MR and Hive on Spark&lt;/P&gt;&lt;P&gt;- If Impala can run your ETL, then it will probably be faster&lt;/P&gt;&lt;P&gt;- Impala will fail/abort a query if a node goes down during query execution&lt;/P&gt;&lt;P&gt;- The last point may make Impala less suitable for long-running jobs, but of course there is also a shorter failure window because queries are faster, so Impala may very well suit&amp;nbsp;your ETL needs if you can tolerate the faiure behavior&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;You may also find this article interesting:&lt;/P&gt;&lt;P&gt;&lt;A href="https://vision.cloudera.com/sql-on-apache-hadoop-choosing-the-right-tool-for-the-right-job/" target="_blank"&gt;https://vision.cloudera.com/sql-on-apache-hadoop-choosing-the-right-tool-for-the-right-job/&lt;/A&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Thu, 04 May 2017 01:08:28 GMT</pubDate>
      <guid>https://community.cloudera.com/t5/Support-Questions/Hive-on-Spark-or-Impala-in-batch-Process-ETL/m-p/54377#M15580</guid>
      <dc:creator>alex.behm</dc:creator>
      <dc:date>2017-05-04T01:08:28Z</dc:date>
    </item>
    <item>
      <title>Re: Hive on Spark or Impala in batch Process (ETL)</title>
      <link>https://community.cloudera.com/t5/Support-Questions/Hive-on-Spark-or-Impala-in-batch-Process-ETL/m-p/300783#M220386</link>
      <description>&lt;P&gt;Hive is more adaptable as far as data arranges that it can check&lt;/P&gt;&lt;P&gt;- You may see Hive as more component wealthy as far as SQL language support and inherent capacities&lt;/P&gt;&lt;P&gt;- Hive will probably finish your inquiry regardless of whether there are hub disappointments (this makes it reasonable for long-running employments); this is valid for both Hive on MR and Hive on Spark&lt;/P&gt;&lt;P&gt;- If Impala can run your &lt;A href="https://www.sprinkledata.com/?utm_source=030820_SparkOrImpala&amp;amp;utm_medium=Cloudera" target="_self"&gt;ETL&lt;/A&gt;, at that point it will most likely be quicker&lt;/P&gt;&lt;P&gt;- Impala will come up short/prematurely end a question if a hub goes down during inquiry execution&lt;/P&gt;&lt;P&gt;- The last point may make Impala less reasonable for long-running occupations, obviously there is likewise a shorter disappointment window since questions are quicker, so Impala might just suit your ETL needs on the off chance that you can endure the faiure conduct&lt;/P&gt;</description>
      <pubDate>Mon, 03 Aug 2020 17:00:32 GMT</pubDate>
      <guid>https://community.cloudera.com/t5/Support-Questions/Hive-on-Spark-or-Impala-in-batch-Process-ETL/m-p/300783#M220386</guid>
      <dc:creator>Henry2410</dc:creator>
      <dc:date>2020-08-03T17:00:32Z</dc:date>
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