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    <title>topic Tracking DBMS CDC in Data Engineering</title>
    <link>https://community.databricks.com/t5/data-engineering/tracking-dbms-cdc/m-p/62595#M32007</link>
    <description>&lt;P&gt;We're using Databricks to incrementally extract data from SQL Server tables into S3. The data contains a timestamp column. We need a place to store the maximum retrieved timestamp per table so it can retrieved during the next run.&lt;/P&gt;&lt;P&gt;Does Databricks contain (or easily connect to) any key-value stores, or have similar functionality? It could of course be tracked using a Delta Lake table but implementing a "frequent updates by primary key" pattern in a columnar storage system seems like a bad idea.&lt;/P&gt;</description>
    <pubDate>Mon, 04 Mar 2024 14:49:08 GMT</pubDate>
    <dc:creator>demost11</dc:creator>
    <dc:date>2024-03-04T14:49:08Z</dc:date>
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      <title>Tracking DBMS CDC</title>
      <link>https://community.databricks.com/t5/data-engineering/tracking-dbms-cdc/m-p/62595#M32007</link>
      <description>&lt;P&gt;We're using Databricks to incrementally extract data from SQL Server tables into S3. The data contains a timestamp column. We need a place to store the maximum retrieved timestamp per table so it can retrieved during the next run.&lt;/P&gt;&lt;P&gt;Does Databricks contain (or easily connect to) any key-value stores, or have similar functionality? It could of course be tracked using a Delta Lake table but implementing a "frequent updates by primary key" pattern in a columnar storage system seems like a bad idea.&lt;/P&gt;</description>
      <pubDate>Mon, 04 Mar 2024 14:49:08 GMT</pubDate>
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      <dc:creator>demost11</dc:creator>
      <dc:date>2024-03-04T14:49:08Z</dc:date>
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