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Use case
Connect data in-place to accelerate AI and advanced analytics with world-class performance on open table formats.
Explore more Enterprise Lakehouse use cases:
Overview
Snowflake’s single, unified platform provides governed access to virtually all lakehouse data, delivering fast, trusted insights and exceptional price and performance on open table formats.
Go beyond formats and catalogs to get a unified, governed view of your data estate — without the complexity of moving and copying data.
Accelerate your roadmap with Snowflake's elastic engine, which separates compute and adaptive concurrency to offer reliable speed without intervention.
Scale your team’s impact by equipping nontechnical users with governed analytics, AI and data products they can trust.
Benefits
Fast analytics on data anywhere
Access existing or easily create Apache IcebergTM tables across catalogs, regions, and clouds without double compute.
Achieve performant, reliable analytics — without the complexity — by querying existing Delta and Apache Parquet files in place in addition to Iceberg tables.
Enrich insights with unstructured data with Documents AI and trusted external knowledge from reputable publishers like the AP, Washington Post, Stripe and more with Cortex Knowledge Extensions.
ANALYZE AT ENTERPRISE SPEED
Achieve 2X faster query performance¶ 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.
Indeed Reimagines Architecture and Data Collaboration to Help Job Seekers and Employers
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.
Harmonize Your Data
Break down data silos and enhance collaboration with Secure Data Sharing for open table formats.
Improve SLAs on dashboards and applications with native integrations.
Deliver AI- and ML-powered insights faster with Snowflake’s fully managed Cortex AI service.
Resources
Get Started
Lakehouse Analytics
Most common questions about Snowflake's approach to data lakehouse analytics, including querying data in place, managing Iceberg tables and using open catalog solutions.
A data lakehouse integrates the cost-effective scalability and flexibility of a data lake, ideal for storing diverse raw data, with the robust data management, governance, and high-performance analytics features typically found in a data warehouse.
Snowflake allows you to analyze data stored directly in your external cloud storage (like Amazon S3, Azure Blob Storage or Google Cloud Storage) without needing to move or copy it into Snowflake. This is primarily done using:
The main difference lies in who manages the Iceberg table's metadata (catalog) and lifecycle.
Snowflake-managed Iceberg Tables: Snowflake Horizon catalog manages and performs operations like compaction. This offers a tightly integrated experience with read/write access directly from Snowflake. For multi-engine read access, sync these tables with Snowflake Open Catalog.
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