Understanding Snowflake Table Types: A Comprehensive Guide

Understanding Snowflake Table Types: Choosing the right table for tasks.
May 29, 2024

Snowflake supports several types of tables to cater to different data storage and management needs. This guide will walk you through the main table types in Snowflake, their characteristics, and use cases.

What are the different types of tables in Snowflake?

Snowflake offers a variety of table types to meet diverse data storage and management requirements. These include Permanent Tables, Temporary Tables, Transient Tables, External Tables, Hybrid Tables, and Iceberg Tables. Each table type has unique characteristics that make it suitable for specific use cases.

1. Permanent Tables

Permanent tables are the default and most common table type in Snowflake. They are designed for storing long-term data that requires high availability, data protection, and recovery mechanisms.


This code creates a permanent table named STORE with columns STORE_ID and STORE_NAME. Permanent tables support Time Travel up to 90 days and have a 7-day fail-safe period for data recovery.

2. Temporary Tables

Temporary tables are designed for storing transient, non-permanent data that is only needed for a specific session or short-term operation.

student_id INT PRIMARY KEY,
name VARCHAR(100),
age INT,
major VARCHAR(100)

This code creates a temporary table named temp_students. Temporary tables only exist within the current session and are automatically dropped when the session ends.

3. Transient Tables

Transient tables are a hybrid between permanent and temporary tables. They persist until explicitly dropped but do not have the same level of data protection as permanent tables.


This code creates a transient table named T_TABLE. Transient tables have a limited Time Travel period of 0-1 day and no fail-safe period.

4. External Tables

External tables are used to query data stored in external cloud storage (e.g., AWS S3, Azure Blob Storage) from within Snowflake.

External tables are read-only and do not support Time Travel or fail-safe capabilities. They are useful for accessing and querying data lakes without ingesting data into Snowflake.

5. Hybrid Tables

Hybrid tables combine columnar key-value pair databases and caching. They are designed for transactional and operational workloads that require high throughput and low latency on small random reads and writes.

  • Fast, single-row operations: Enable fast, single-row operations for lightweight transactional use cases.
  • Unique constraints: Enforce unique constraints for required primary keys, allowing referential integrity constraints.
  • Indexing: Include indexes to retrieve data faster.
  • Integrity checks: Offer integrity checks to ensure data accuracy.

6. Iceberg Tables

Iceberg tables store data and metadata files in external cloud storage, such as Amazon S3, Google Cloud Storage, or Azure Storage. The data is stored in Apache Iceberg table format.

  • Multi-table transactions: Support deleting customer data from multiple Iceberg tables in a single transaction.
  • Row- and column-level security: Implement row- and column-level security.
  • Faster querying: Designed to increase querying speed and efficiency with features like fast scan planning and pruning metadata files.

Comparison of Snowflake Table Types

Table Type Persistence Time Travel Fail-Safe Use Case Permanent Long-term Up to 90 days 7 days Core business data Temporary Session-based 0-1 day None Short-term operations Transient Until explicitly dropped 0-1 day None Intermediate storage External External storage None None Data lakes Hybrid Until explicitly dropped Varies Varies Transactional workloads Iceberg External storage Varies Varies Large datasets

Common Challenges and Solutions

While working with Snowflake tables, you may encounter some common challenges. Here are a few and their solutions:

  • Challenge: Data recovery issues with transient tables. Solution: Use permanent tables for critical data that requires robust recovery options.
  • Challenge: Performance issues with large datasets. Solution: Use clustering keys and partitioning to optimize query performance.
  • Challenge: Managing session-based data with temporary tables. Solution: Ensure proper session management and cleanup to avoid data loss.

Recap of Snowflake Table Types

In this guide, we explored the different types of tables in Snowflake, including their characteristics and use cases. Understanding these table types will help you make informed decisions based on your data storage and management needs.

  • Permanent tables are ideal for long-term, critical data storage with robust recovery options.
  • Temporary and transient tables are suitable for short-term or intermediate data processing tasks.
  • External, hybrid, and iceberg tables offer specialized solutions for specific use cases like data lakes, transactional workloads, and large datasets.

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