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    <title>topic Re: Delta Table in DLT pipeline in Data Engineering</title>
    <link>https://community.databricks.com/t5/data-engineering/delta-table-in-dlt-pipeline/m-p/161476#M55025</link>
    <description>&lt;P class="p1"&gt;&lt;SPAN class="s1"&gt;Hi, yes, both streaming tables and materialized views in a DLT pipeline (now called Lakeflow Declarative&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;&lt;SPAN class="Apple-converted-space"&gt;&amp;nbsp;&lt;/SPAN&gt;Pipelines) are Delta tables under the hood. There isn't a separate "plain Delta table" object type inside a&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;&lt;SPAN class="Apple-converted-space"&gt;&amp;nbsp;&lt;/SPAN&gt;pipeline, streaming tables and materialized views are the two ways a pipeline writes Delta tables, they just&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;&lt;SPAN class="Apple-converted-space"&gt;&amp;nbsp;&lt;/SPAN&gt;differ in how the data gets refreshed.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P class="p1"&gt;&lt;SPAN class="s1"&gt;If you specifically want a table that behaves like a plain batch table with no streaming semantics, a&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;&lt;SPAN class="Apple-converted-space"&gt;&amp;nbsp;&lt;/SPAN&gt;materialized view is the closest equivalent since it's a full batch recompute each time rather than a continuous&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;append.&lt;/SPAN&gt;&lt;/P&gt;</description>
    <pubDate>Sun, 05 Jul 2026 13:16:36 GMT</pubDate>
    <dc:creator>iyashk-DB</dc:creator>
    <dc:date>2026-07-05T13:16:36Z</dc:date>
    <item>
      <title>Delta Table in DLT pipeline</title>
      <link>https://community.databricks.com/t5/data-engineering/delta-table-in-dlt-pipeline/m-p/161418#M55022</link>
      <description>&lt;P&gt;Hi all, Is it possible to create a Delta table using DLT pipeline. I'm able to create a Delta table using Job and also able to create Materialized view and Streaming table using DLT pipleines. But I want to create Delta table using DLT pipelines. Is it possible and how can we create that?&lt;BR /&gt;Any help is very much appreciated, Thanks&lt;/P&gt;&lt;P&gt;this is how delta table looks in databricks, attaching in media&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Sun, 05 Jul 2026 05:12:48 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/delta-table-in-dlt-pipeline/m-p/161418#M55022</guid>
      <dc:creator>habs</dc:creator>
      <dc:date>2026-07-05T05:12:48Z</dc:date>
    </item>
    <item>
      <title>Re: Delta Table in DLT pipeline</title>
      <link>https://community.databricks.com/t5/data-engineering/delta-table-in-dlt-pipeline/m-p/161476#M55025</link>
      <description>&lt;P class="p1"&gt;&lt;SPAN class="s1"&gt;Hi, yes, both streaming tables and materialized views in a DLT pipeline (now called Lakeflow Declarative&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;&lt;SPAN class="Apple-converted-space"&gt;&amp;nbsp;&lt;/SPAN&gt;Pipelines) are Delta tables under the hood. There isn't a separate "plain Delta table" object type inside a&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;&lt;SPAN class="Apple-converted-space"&gt;&amp;nbsp;&lt;/SPAN&gt;pipeline, streaming tables and materialized views are the two ways a pipeline writes Delta tables, they just&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;&lt;SPAN class="Apple-converted-space"&gt;&amp;nbsp;&lt;/SPAN&gt;differ in how the data gets refreshed.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P class="p1"&gt;&lt;SPAN class="s1"&gt;If you specifically want a table that behaves like a plain batch table with no streaming semantics, a&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;&lt;SPAN class="Apple-converted-space"&gt;&amp;nbsp;&lt;/SPAN&gt;materialized view is the closest equivalent since it's a full batch recompute each time rather than a continuous&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="s1"&gt;append.&lt;/SPAN&gt;&lt;/P&gt;</description>
      <pubDate>Sun, 05 Jul 2026 13:16:36 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/delta-table-in-dlt-pipeline/m-p/161476#M55025</guid>
      <dc:creator>iyashk-DB</dc:creator>
      <dc:date>2026-07-05T13:16:36Z</dc:date>
    </item>
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