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    <title>topic Announcement | NBCUniversal’s Seamless Migration: Unlocking Scalable Analytics with Databricks in Announcements</title>
    <link>https://community.databricks.com/t5/announcements/announcement-nbcuniversal-s-seamless-migration-unlocking/m-p/164730#M962</link>
    <description>&lt;P&gt;&lt;SPAN&gt;NBCUniversal partnered with EXL and Databricks to modernize its data platform, moving from a shared slot-based model to Databricks job-specific compute. The migration helped NBCUniversal reduce data infrastructure costs by &lt;/SPAN&gt;&lt;STRONG&gt;30%&lt;/STRONG&gt;&lt;SPAN&gt;, onboard more than &lt;/SPAN&gt;&lt;STRONG&gt;300 analysts&lt;/STRONG&gt;&lt;SPAN&gt; to Databricks SQL, and create a more flexible foundation for analytics, machine learning, and data engineering.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;FONT size="4"&gt;&lt;STRONG&gt;Key highlights&lt;/STRONG&gt;&lt;/FONT&gt;&lt;/P&gt;
&lt;UL&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;30% lower infrastructure costs:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;Dedicated compute lets pipelines scale independently instead of competing for shared reserved capacity.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;A unified analytics platform:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;Data engineering, SQL analytics, machine learning, and workflow orchestration now run together on Databricks.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;Phased migration with EXL:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;Custom tools supported discovery, SQL conversion, data transfer, orchestration migration, and automated validation.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;Safer cutover process:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;NBCUniversal ran workloads in parallel, compared results, and migrated in waves before retiring legacy workloads.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;Ready for changing demand:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;The new architecture gives teams more flexibility to scale around major content launches, awards shows, and live events without pre-reserving capacity for every workload.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;/UL&gt;
&lt;P class="p8i6j01 paragraph"&gt;&lt;A style="background-color: #ff3621; color: white; padding: 10px 20px; text-decoration: none; border-radius: 5px; font-weight: bold; display: inline-block;" href="https://www.databricks.com/blog/nbcuniversals-seamless-migration-unlocking-scalable-analytics-databricks?utm_source=bambu&amp;amp;utm_medium=social&amp;amp;utm_campaign=advocacy" target="_blank" rel="noopener"&gt; &lt;span class="lia-unicode-emoji" title=":backhand_index_pointing_right:"&gt;👉&lt;/span&gt; Read the full post here &lt;/A&gt;&lt;/P&gt;</description>
    <pubDate>Mon, 03 Aug 2026 11:22:19 GMT</pubDate>
    <dc:creator>Tushar_Parekar</dc:creator>
    <dc:date>2026-08-03T11:22:19Z</dc:date>
    <item>
      <title>Announcement | NBCUniversal’s Seamless Migration: Unlocking Scalable Analytics with Databricks</title>
      <link>https://community.databricks.com/t5/announcements/announcement-nbcuniversal-s-seamless-migration-unlocking/m-p/164730#M962</link>
      <description>&lt;P&gt;&lt;SPAN&gt;NBCUniversal partnered with EXL and Databricks to modernize its data platform, moving from a shared slot-based model to Databricks job-specific compute. The migration helped NBCUniversal reduce data infrastructure costs by &lt;/SPAN&gt;&lt;STRONG&gt;30%&lt;/STRONG&gt;&lt;SPAN&gt;, onboard more than &lt;/SPAN&gt;&lt;STRONG&gt;300 analysts&lt;/STRONG&gt;&lt;SPAN&gt; to Databricks SQL, and create a more flexible foundation for analytics, machine learning, and data engineering.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;FONT size="4"&gt;&lt;STRONG&gt;Key highlights&lt;/STRONG&gt;&lt;/FONT&gt;&lt;/P&gt;
&lt;UL&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;30% lower infrastructure costs:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;Dedicated compute lets pipelines scale independently instead of competing for shared reserved capacity.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;A unified analytics platform:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;Data engineering, SQL analytics, machine learning, and workflow orchestration now run together on Databricks.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;Phased migration with EXL:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;Custom tools supported discovery, SQL conversion, data transfer, orchestration migration, and automated validation.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;Safer cutover process:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;NBCUniversal ran workloads in parallel, compared results, and migrated in waves before retiring legacy workloads.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI style="font-weight: 400;" aria-level="1"&gt;&lt;STRONG&gt;Ready for changing demand:&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;The new architecture gives teams more flexibility to scale around major content launches, awards shows, and live events without pre-reserving capacity for every workload.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;/UL&gt;
&lt;P class="p8i6j01 paragraph"&gt;&lt;A style="background-color: #ff3621; color: white; padding: 10px 20px; text-decoration: none; border-radius: 5px; font-weight: bold; display: inline-block;" href="https://www.databricks.com/blog/nbcuniversals-seamless-migration-unlocking-scalable-analytics-databricks?utm_source=bambu&amp;amp;utm_medium=social&amp;amp;utm_campaign=advocacy" target="_blank" rel="noopener"&gt; &lt;span class="lia-unicode-emoji" title=":backhand_index_pointing_right:"&gt;👉&lt;/span&gt; Read the full post here &lt;/A&gt;&lt;/P&gt;</description>
      <pubDate>Mon, 03 Aug 2026 11:22:19 GMT</pubDate>
      <guid>https://community.databricks.com/t5/announcements/announcement-nbcuniversal-s-seamless-migration-unlocking/m-p/164730#M962</guid>
      <dc:creator>Tushar_Parekar</dc:creator>
      <dc:date>2026-08-03T11:22:19Z</dc:date>
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