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   Powering Analytics and AI on the Lakehouse
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   Hi there,
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    If you have data sitting in a lakehouse, you know that turning that raw data into fast, reliable production-grade analytics is a significant challenge.
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    To get predictable performance and reliable concurrency, teams often find themselves managing complex Spark clusters, tuning file layouts or building brittle ETL pipelines just to move data around.
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   <br/>
   Join our upcoming demo,
   <strong>
    <a id="" target="_blank">
     Bring Snowflake to Your Data: Powering Analytics and AI on the Lakehouse
    </a>
   </strong>
   , to see how you can bring Snowflake’s elastic, performant engine directly to your data lake. You will discover how to unify analytics across open file formats using interoperable storage and compute architecture — without migration or re-architecture.
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    In this demo on March 26 at 10 a.m. PT, you will learn how to:
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       Connect and query
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      : Instantly query raw Parquet and Iceberg data residing in object storage without moving it.
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     <p>
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       Accelerate
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      : Deliver sub-second dashboard performance on lake data using an engine that automatically handles concurrency and caching.
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    <li>
     <p>
      <strong>
       Govern
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      : Apply Snowflake’s native row-level security and governance policies directly to your external data lake.
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      <strong>
       Build
      </strong>
      : Create production-ready data pipelines using Python and SQL that span both managed storage and open file formats.
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   </ul>
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    <strong>
     <a id="" target="_blank">
      Register now to participate
     </a>
    </strong>
    .
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    Best regards,
    <br/>
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    The Snowflake Team
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    You're receiving this email because you're signed up to receive updates from Snowflake. If you'd prefer not to receive updates, you can unsubscribe
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    .
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    Snowflake | Suite 3A, 106 East Babcock Street, Bozeman, Montana 59715
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