Section 2.1: Temporary Tables

Temporary tables serve as useful tools during intricate operations. They help streamline queries, allowing for efficient processing of intermediate results without overwhelming long-term storage.

Use Cases:

• Scenario: Retaining temporary results during complex query execution.
• Example: Storing datasets for analysis or report generation.

Pros:

• Query Optimization: Breaks down complex queries into simpler steps.
• Storage Efficiency: Minimizes the need for lasting storage of temporary results.

Cons:

• Ephemeral Data: Data in temporary tables is lost after sessions end.
• Session Management: Requires careful management to avoid unintended data loss.

SQL Code Sample:

CREATE TEMPORARY TABLE temp_table AS

SELECT id, data

FROM source_table

WHERE condition;

Section 2.2: Permanent Tables

Permanent tables act as stable foundations for data storage, ensuring that operational data remains accessible and intact over time.

Use Cases:

• Scenario: A standard choice for long-term data retention.
• Example: Maintaining operational data for extended periods.

Pros:

• Durable and Reliable: Guarantees dependable long-term storage.
• Operational Optimization: Well-suited for everyday operational data.

Cons:

• Higher Storage Costs: Keeping historical data can lead to increased expenses.
• Archiving Needs: Necessitates thoughtful archiving strategies.

SQL Code Sample:

CREATE TABLE permanent_table (

id INT,

data STRING

);

Section 2.3: Transient Tables

Transient tables act as efficient handlers for large datasets during particular operations, ensuring smooth processing without affecting overall storage.

Use Cases:

• Scenario: Temporary storage of vast datasets during specific tasks.
• Example: A staging area for data processing.

Pros:

• Efficient Data Handling: Manages large volumes without straining overall storage.
• Operational Optimization: Enhances processes involving significant data manipulation.

Cons:

• Ephemeral Data: Data in transient tables is not retained across sessions.
• Session Management: Careful handling is necessary to prevent data loss.

SQL Code Sample:

CREATE TRANSIENT TABLE transient_table AS

SELECT id, data

FROM source_table

WHERE condition;

Section 3.1: Hybrid Tables

Hybrid tables represent a pioneering table type in Snowflake, known as "Unistore." This innovative approach seamlessly merges transactional and analytical data on a single platform.

Use Cases:

• Scenario: Combining structured and semi-structured data.
• Example: Data that includes both relational and non-relational attributes.

Pros:

• Unified Storage Solution: Effectively manages various data types within one table.
• Simplified Queries: Eases querying across different data types.

Cons:

• Schema Design Complexity: Balancing diverse data structures can be challenging.
• Performance Considerations: Requires careful querying strategies.

Key Features of Hybrid Tables:

• Transactional Capabilities: Designed to support all transactional features, ideal for applications demanding quick operations.
• Performance Prowess: Excels in rapid single-row operations.
• Row-Based Storage Engine: Unlike other Snowflake tables, hybrid tables utilize a new row-based storage engine for improved efficiency.

The anticipated Hybrid tables, along with their capabilities, mark a significant step in Snowflake’s progress, promising to change how organizations manage varied data types.

Section 3.2: Directory Tables

Directory tables function like organized filing cabinets, maintaining a structured hierarchy that simplifies data access.

Use Cases:

• Scenario: Data organized in folders and subfolders.
• Example: Storing data in a hierarchical structure.

Pros:

• Efficient Organization: Ideal for managing extensive data volumes.
• Integration Ease: Smoothly connects with external systems that follow a directory structure.

Cons:

• Limited Performance: May face challenges with certain queries.
• Optimization Issues: Deeply nested structures can complicate optimization.

Section 3.3: External Tables

External tables bridge the gap between your Snowflake warehouse and external data sources, such as data lakes.

Use Cases:

• Scenario: Data stored outside Snowflake (e.g., S3, Azure Blob Storage).
• Example: Connecting with data lakes or other external platforms.

Pros:

• Seamless Integration: Access external data without replication.
• Unified Data View: Offers a comprehensive view of both internal and external data.

Cons:

• Latency Considerations: Accessing external data may introduce delays.
• Limited Control: Less control compared to internal tables.

SQL Code Sample:

CREATE EXTERNAL TABLE external_table (

id INT,

name STRING

)

LOCATION = @my_stage;

Section 3.4: Iceberg Tables

Iceberg tables allow for efficient management of historical data with support for incremental updates.

Use Cases:

• Scenario: Large datasets needing efficient incremental updates.
• Example: Maintaining historical data while continuously adding new entries.

Pros:

• Optimized for Time-Travel Queries: Facilitates historical analysis.
• Incremental Update Support: Efficiently supports “merge-on-read” for ongoing updates.

Cons:

• Metadata Management: Requires careful management of metadata files.
• Schema Evolution Considerations: Changes in data schema require careful planning.

Iceberg tables are now publicly available for all accounts, enabling the integration of external cloud storage with Snowflake’s analytical capabilities.

To learn more about Iceberg tables, check the following resources:

• Build an Open Data Lakehouse with Iceberg Tables
• Snowflake Documentation on Iceberg Tables