Hi Robert,
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.
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.
Join our upcoming demo, Bring Snowflake to Your Data: Powering Analytics and AI on the Lakehouse <https://go.snowflake.net/MjUyLVJGTy0yMjcAAAGgCy6IDorTR7kLXIHYhmi430CKm3H2iQQ96JjhBDN9Anly4DB_k1p3DhIeLW0MhVk1bj6UTpo=>, 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.
In this demo on March 26 at 10 a.m. PT, you will learn how to:
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Connect and query: Instantly query raw Parquet and Iceberg data residing in ob