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    <title>topic Re: Automate Lakeflow connect to ingest 300 tables not manually in Data Engineering</title>
    <link>https://community.databricks.com/t5/data-engineering/automate-lakeflow-connect-to-ingest-300-tables-not-manually/m-p/158067#M54656</link>
    <description>&lt;P&gt;Configuring &lt;STRONG&gt;Databricks Lake flow Connect for PostgreSQL&lt;/STRONG&gt; is a streamlined, multi-step seamless process and you can ingest multiple tables within a single pipeline.&lt;/P&gt;&lt;P&gt;You can follow below&lt;/P&gt;&lt;H3&gt;Selecting Multiple Tables via the UI&lt;/H3&gt;&lt;P&gt;In the pipeline creation wizard where you will select your tables in the &lt;STRONG&gt;Source&lt;/STRONG&gt; step (&lt;I&gt;"Specify what data to ingest" - 3rd step&lt;/I&gt;).&lt;/P&gt;&lt;UL&gt;&lt;LI&gt;&lt;P&gt;You can check the boxes for all the tables you want to include.&lt;/P&gt;&lt;/LI&gt;&lt;LI&gt;&lt;P&gt;For each selected table, you can individually configure specific settings such as &lt;STRONG&gt;Primary Keys&lt;/STRONG&gt; and &lt;STRONG&gt;History Tracking&lt;/STRONG&gt; (SCD behavior). Ensure the post gres schema &amp;amp; tables are configured before creating a pipeline in Lakeflow Connect&lt;/P&gt;&lt;/LI&gt;&lt;/UL&gt;&lt;H3&gt;Scalability &amp;amp; Limits&lt;/H3&gt;&lt;UL&gt;&lt;LI&gt;&lt;P&gt;&lt;STRONG&gt;Table Limits:&lt;/STRONG&gt; Databricks recommends configuring &lt;STRONG&gt;250 or fewer tables per pipeline&lt;/STRONG&gt; to ensure optimal performance and manageability. If you need to ingest more than 250 tables, you can split them across multiple pipelines grouping by domain or schema.&amp;nbsp;&lt;SPAN&gt;More details &lt;/SPAN&gt;&lt;A style="background-color: #ffffff;" href="https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/postgresql-limits#tables" target="_blank" rel="noopener"&gt;here&lt;/A&gt;&lt;/P&gt;&lt;/LI&gt;&lt;LI&gt;&lt;P&gt;&lt;STRONG&gt;Data Volume:&lt;/STRONG&gt; There is &lt;STRONG&gt;no limit&lt;/STRONG&gt; on the number of rows or columns supported within these tables.&lt;/P&gt;&lt;/LI&gt;&lt;/UL&gt;&lt;H3&gt;YAML&lt;/H3&gt;&lt;P&gt;You can configure your multi table Lake flow pipelines using &lt;STRONG&gt;YAML configuration&amp;nbsp;&lt;/STRONG&gt;if you prefer configuration&amp;nbsp;to ensure reproducibility. More details &lt;A href="https://dzone.com/articles/lakeflow-connect-postgresql-integration-tutorial" target="_self"&gt;here&lt;/A&gt;&lt;/P&gt;</description>
    <pubDate>Mon, 01 Jun 2026 16:53:07 GMT</pubDate>
    <dc:creator>balajij8</dc:creator>
    <dc:date>2026-06-01T16:53:07Z</dc:date>
    <item>
      <title>Automate Lakeflow connect to ingest 300 tables not manually</title>
      <link>https://community.databricks.com/t5/data-engineering/automate-lakeflow-connect-to-ingest-300-tables-not-manually/m-p/158058#M54655</link>
      <description>&lt;P&gt;I have data in PostgreSQL and I’m using Lakeflow Connect via UI to ingest it into Databricks streaming tables.&lt;/P&gt;&lt;P&gt;Currently, each Lakeflow Connect pipeline only allows connecting one PostgreSQL table. I have around 300 tables, and creating pipelines manually for each table is time-consuming.&lt;/P&gt;&lt;P&gt;I’m looking for a way to automate this process, where I can provide a PostgreSQL connection and table names (or a list/schema), and automatically generate and deploy the required Lakeflow Connect pipelines.&lt;/P&gt;&lt;P&gt;I explored Asset Bundles and YAML-based definitions, but it seems Lakeflow Connect resources are not fully supported there yet.&lt;/P&gt;&lt;P&gt;What would be a scalable or recommended approach to design this setup in Databricks?&lt;/P&gt;</description>
      <pubDate>Mon, 01 Jun 2026 15:06:56 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/automate-lakeflow-connect-to-ingest-300-tables-not-manually/m-p/158058#M54655</guid>
      <dc:creator>muaaz</dc:creator>
      <dc:date>2026-06-01T15:06:56Z</dc:date>
    </item>
    <item>
      <title>Re: Automate Lakeflow connect to ingest 300 tables not manually</title>
      <link>https://community.databricks.com/t5/data-engineering/automate-lakeflow-connect-to-ingest-300-tables-not-manually/m-p/158067#M54656</link>
      <description>&lt;P&gt;Configuring &lt;STRONG&gt;Databricks Lake flow Connect for PostgreSQL&lt;/STRONG&gt; is a streamlined, multi-step seamless process and you can ingest multiple tables within a single pipeline.&lt;/P&gt;&lt;P&gt;You can follow below&lt;/P&gt;&lt;H3&gt;Selecting Multiple Tables via the UI&lt;/H3&gt;&lt;P&gt;In the pipeline creation wizard where you will select your tables in the &lt;STRONG&gt;Source&lt;/STRONG&gt; step (&lt;I&gt;"Specify what data to ingest" - 3rd step&lt;/I&gt;).&lt;/P&gt;&lt;UL&gt;&lt;LI&gt;&lt;P&gt;You can check the boxes for all the tables you want to include.&lt;/P&gt;&lt;/LI&gt;&lt;LI&gt;&lt;P&gt;For each selected table, you can individually configure specific settings such as &lt;STRONG&gt;Primary Keys&lt;/STRONG&gt; and &lt;STRONG&gt;History Tracking&lt;/STRONG&gt; (SCD behavior). Ensure the post gres schema &amp;amp; tables are configured before creating a pipeline in Lakeflow Connect&lt;/P&gt;&lt;/LI&gt;&lt;/UL&gt;&lt;H3&gt;Scalability &amp;amp; Limits&lt;/H3&gt;&lt;UL&gt;&lt;LI&gt;&lt;P&gt;&lt;STRONG&gt;Table Limits:&lt;/STRONG&gt; Databricks recommends configuring &lt;STRONG&gt;250 or fewer tables per pipeline&lt;/STRONG&gt; to ensure optimal performance and manageability. If you need to ingest more than 250 tables, you can split them across multiple pipelines grouping by domain or schema.&amp;nbsp;&lt;SPAN&gt;More details &lt;/SPAN&gt;&lt;A style="background-color: #ffffff;" href="https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/postgresql-limits#tables" target="_blank" rel="noopener"&gt;here&lt;/A&gt;&lt;/P&gt;&lt;/LI&gt;&lt;LI&gt;&lt;P&gt;&lt;STRONG&gt;Data Volume:&lt;/STRONG&gt; There is &lt;STRONG&gt;no limit&lt;/STRONG&gt; on the number of rows or columns supported within these tables.&lt;/P&gt;&lt;/LI&gt;&lt;/UL&gt;&lt;H3&gt;YAML&lt;/H3&gt;&lt;P&gt;You can configure your multi table Lake flow pipelines using &lt;STRONG&gt;YAML configuration&amp;nbsp;&lt;/STRONG&gt;if you prefer configuration&amp;nbsp;to ensure reproducibility. More details &lt;A href="https://dzone.com/articles/lakeflow-connect-postgresql-integration-tutorial" target="_self"&gt;here&lt;/A&gt;&lt;/P&gt;</description>
      <pubDate>Mon, 01 Jun 2026 16:53:07 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/automate-lakeflow-connect-to-ingest-300-tables-not-manually/m-p/158067#M54656</guid>
      <dc:creator>balajij8</dc:creator>
      <dc:date>2026-06-01T16:53:07Z</dc:date>
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