Bring Snowflake to Your Data: Powering Analytics and AI on the Lakehouse

You have data sitting in a lakehouse—Parquet files, Delta Lake or Apache Iceberg—but turning that data into fast, reliable production-grade analytics is still a challenge. 

You need predictable performance, reliable concurrency and governed access at scale. Too often, that comes with managing Spark clusters, tuning file layouts or building brittle ETL pipelines just to move data around.

Join this demo to see how Snowflake brings its elastic, performant engine directly to your data lake. You’ll learn how Snowflake unifies analytics across open file formats using its interoperable storage and compute architecture without migration or re-architecture.

In this demo, you will learn:
 

• Connect & Query: How you can instantly query raw Parquet and Iceberg data residing in object storage without moving it.

• Govern: How you can apply Snowflake’s native row-level security and governance policies to your external data lake.

• Accelerate: How Snowflake’s engine automatically handles concurrency and caching to deliver sub-second dashboard performance on lake data.

• Build: How you can create a production-ready data pipeline using Python and SQL that spans both managed storage and open file formats.