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Architecting an AI-Ready Lakehouse for Enterprise Intelligence
Learn how Whoop powers governed, AI-ready insights for every team.
Use case
Power Analytics and AI Without Moving Your Data
Access your entire data estate, wherever it lives. No pipelines, no copies, no re-architecture.
Explore more Enterprise Lakehouse use cases:
Overview
Stop managing multiple engines. Instead, use just one for analytics and AI, no data movement required.
Give your team a unified platform to support SLAs, act on high-fidelity data for analytics and AI workloads,and deliver insights without the sprawl.
Access data in place
Don’t worry about data duplication. Act on Iceberg, Delta and Parquet data wherever it lives, for a connected view of your data estate.
Empower every persona
Let anyone on your team, from analysts to engineers, self-serve insights from your data with built-in agentic intelligence, while preserving unified governance and security controls.
Control costs
Only pay for the compute you use. Snowflake elastically scales, suspending when idle and resuming instantly on demand.
Benefits
Analytics and AIwhere your data already lives
Analytics on open formats, in place
Extend Snowflake's performance, ease of use and reliability without duplicating data
Access existing Apache Icebergtm tables or easily create them across catalogs, regions and clouds.
Achieve performant, reliable analytics by querying existing Delta and Apache Parquet files in place in addition to Iceberg tables.
Connect Snowflake directly to your remote Iceberg catalogs, with automatic discovery and refresh, for a unified, governed view of your data estate.
Faster queries with natural language
Run enterprise-grade analytics, without the overhead
Achieve 2X faster query performance1,2 on Apache Iceberg tables by extending our leading analytics engine to open table formats.
Accelerate time to value and continually streamline workflows with performance improvements that are automatically enabled — no manual tuning required.
- Maintain productivity and system efficiency by running complex joins and queries without resource contention.
Indeed Reimagines Architecture and Data Collaboration to Help Job Seekers and Employers without Data Movement
With Snowflake’s native support for Iceberg tables, Indeed converted its 52-petabyte data lake, breaking down data silos and allowing analysts to directly read and write Iceberg tables through Snowflake.
- 43–74% cost reduction compared to previous analytical tools
Unified governance
Set one governance policy for your full data estate and enforce it everywhere
- Equip all teams with governed, decision-ready data they can trust to support SLAs and drive business outcomes.
Apply fine-grained row and column level security to all your lakehouse data, so every user gets precise access to what they need.
Manage all access policies centrally in Snowflake.
Resources
Explore more for lakehouse analytics
Get Started
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- Enable your most critical data workloads
Lakehouse Analytics
Frequently Asked Questions
Most common questions about Snowflake's approach to lakehouse analytics, including querying data in place, managing Iceberg tables and catalogs.
Do I need to migrate my data or change my existing setup?
No. Snowflake queries data in place across Amazon S3, Azure Data Lake Storage and Google Cloud Storage, so existing catalogs, pipelines and data can stay where they are.
Which open formats and catalogs does Snowflake support?
Snowflake supports Apache Iceberg, Delta Lake and Apache Parquet. It also connects to Iceberg REST-compatible catalogs, including AWS Glue and Databricks Unity Catalog.
What is the difference between Snowflake-managed and externally managed Iceberg tables?
The difference is who manages the table metadata and lifecycle.
Snowflake-managed Iceberg tables
Snowflake serves as the Iceberg catalog and handles maintenance and optimization, including compaction. Teams get read and write access in Snowflake, while engines such as Spark and Trino can access the same tables through Snowflake Horizon Catalog.
Externally managed Iceberg tables
An existing catalog, such as AWS Glue or Databricks Unity Catalog, manages the metadata and lifecycle. Snowflake connects through a catalog-linked database to query and write
Do Snowflake governance controls apply to lake data?
Yes. Masking policies, row access policies and role-based access control are applied to connected lake data through one governance framework across the full data estate.
