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Enterprise cloud data warehouse with native Iceberg catalog, automatic optimization, Snowpipe Streaming, UniForm interoperability, and zero-maintenance table management. Store data in open Iceberg format while benefiting from Snowflake's automatic clustering, compaction, and enterprise security features.
Key Features
Native Catalog Integration
Snowflake catalog (native) with full read/write capabilities. External catalogs (Glue, Open Table Catalog) accessible read-only via catalog integration objects
Enterprise Automatic Optimization
Auto-cluster & auto-compaction services: coalesce small Parquet files, rewrite manifests, merge delete files, update clustering metadata continuously
Catalog-Dependent DML
INSERT, UPDATE, DELETE, MERGE INTO fully ACID on Snowflake-catalog tables. Position-delete files, equality-delete in preview. External tables read-only
Intelligent Storage Management
DML writes merge-on-read delete files. Automatic Storage Optimization compacts files & merges delete files, switching to copy-on-write during clustering
Snowpipe Streaming Integration
Snowpipe Streaming & Storage Write API for real-time ingestion (GA). Streams & Tasks supported on Snowflake-catalog tables. No built-in CDC ingestion
Limited Format Support
Parquet only format support. Iceberg spec v2 for Snowflake-catalog tables; external reads work on v1 or v2. No v3 support yet
Advanced Time Travel
Query snapshots with AT(SNAPSHOT => id) or AT(TIME => ts). Zero-Copy Clones work on Iceberg tables. External tables require explicit REFRESH
Enterprise Security & Governance
Complete Snowflake RBAC, column masking, row-access policies, tag-based masking. Query activity in ACCOUNT_USAGE & ACCESS_HISTORY. Customer-managed IAM
UniForm Interoperability
UniForm exposes Snowflake tables through Iceberg-compatible REST catalog so external engines (Spark, Trino) can read them. Cross-cloud support via External Volumes
Advanced Enterprise Features
Search Optimization, micro-partition clustering, Zero-Copy Cloning, vectorized Parquet scanner with manifest pruning for high performance on Snowflake-catalog tables
Snowflake Iceberg Feature Matrix
Comprehensive breakdown of Iceberg capabilities in Snowflake across catalog integration, DML operations, streaming support, and enterprise security. Shows feature support levels, implementation details, and availability status.
Dimension | Support Level | Implementation Details | Availability |
|---|---|---|---|
Catalog Types | PartialNative Full, External Read | Snowflake native catalog (full read/write) + external catalogs (read-only via integration) | GA |
SQL Analytics | PartialCatalog Dependent | Native: full DDL/DML, transactions, Snowflake features; External: SELECT only | GA |
DML Operations | PartialNative Only | INSERT/UPDATE/DELETE/MERGE with ACID on native; position-deletes; equality-deletes preview | GA |
Storage Strategy | FullAdaptive Auto | MoR writes + automatic CoW optimization; background clustering & compaction | GA |
Streaming Support | FullSnowpipe GA | Snowpipe Streaming + Storage Write API (GA); Streams & Tasks on native tables | GA |
Format Support | LimitedParquet v2 Only | Parquet only; spec v2 for native tables; v1/v2 read for external; no v3 support | GA |
Time Travel | FullEnterprise Features | AT(SNAPSHOT/TIME) syntax; Zero-Copy Clones; external tables need REFRESH | GA |
Schema Evolution | FullMetadata-only | ADD/DROP/RENAME columns, type widening, nullability changes; atomic snapshots | GA |
Security & Governance | FullEnterprise RBAC | Complete RBAC, column/row masking, tag policies; ACCOUNT_USAGE audit | GA |
Automatic Optimization | FullZero Maintenance | Auto-clustering, compaction, delete-file merging; continuous background optimization | GA |
UniForm Interoperability | InnovativeExternal Engine Access | Exposes Snowflake tables via Iceberg REST catalog to Spark/Trino (read-only) | GA |
Enterprise Features | FullAdvanced Capabilities | Search Optimization, micro-partitioning, Zero-Copy Clones, vectorized scanner | GA |
Showing 12 entries
For issues, click here (GitHub)Use Cases
Enterprise Data Warehouse
Full-featured data warehouse with native Iceberg integration
- Real-world example: A telecommunications company manages 10TB of customer data in Snowflake Iceberg tables. Snowflake's automatic clustering continuously reorganizes data based on query patterns, while auto-compaction merges small files in the background. The data team focuses on analytics instead of table maintenance, saving 20+ hours per week of manual optimization work
- Modern data warehouse with zero maintenance optimization for production workloads
- Enterprise environments requiring comprehensive RBAC and governance controls
- Multi-tenant deployments with fine-grained security and isolation
Real-time Analytics with Snowpipe
Streaming ingestion and change processing workflows
- Real-world example: An IoT platform ingests sensor data from 50,000 devices using Snowpipe Streaming into Iceberg tables. Data becomes queryable within seconds of arrival, powering real-time alerting dashboards. When anomalies are detected, Streams and Tasks automatically trigger data quality checks and send notifications, all within Snowflake's Iceberg ecosystem
- Real-time data warehouse updates with Snowpipe Streaming for operational analytics
- Change data capture with Streams and Tasks for automated processing pipelines
- High-throughput streaming analytics with near real-time dashboard updates
Multi-Engine Data Architecture
UniForm interoperability for diverse analytical tools
- Real-world example: A media company stores video analytics data in Snowflake Iceberg tables. Their data science team uses Snowflake SQL for business intelligence, while their ML engineers use Apache Spark (accessing via UniForm) for model training. Both teams work with the same data without ETL pipelines or data duplication, reducing costs and eliminating sync issues
- Data sharing between Snowflake and external engines (Spark, Trino) without duplication
- Hybrid analytical architectures with multiple processing engines and tools
- Cross-cloud and cross-region data access scenarios with unified governance
Development and Testing Optimization
Zero-Copy Cloning for efficient development workflows
- Real-world example: A SaaS company uses Zero-Copy Cloning to create instant copies of production Iceberg tables for testing. Developers can experiment with schema changes, test new features, and validate data transformations on production-scale data without consuming additional storage or waiting for lengthy copy operations. When testing completes, they simply drop the clones
- Instant development and testing environments with clones for rapid iteration
- Data science experimentation without storage costs or data duplication overhead
- Backup and recovery scenarios with time travel for disaster recovery
Resources & Documentation
Official Documentation
Complete API reference and guides
Getting Started Guide
Quick start tutorials and examples
Apache Iceberg Tables Overview
Documentation
CREATE ICEBERG TABLE
Documentation
Manage Iceberg Tables
Documentation
Iceberg Storage Management
Documentation
Snowpipe Streaming with Iceberg
Documentation
Time Travel Documentation
Documentation
Access Control Privileges
Documentation
Apache Iceberg v3 Blog
Documentation