Table

A Table is the basic unit for storing data in the Lakehouse, using columnar storage (Parquet format) suitable for analytical queries.

Unlike Dynamic Tables and Materialized Views, a table's data must be maintained manually through INSERT, UPDATE, DELETE, or COPY INTO — the system will not auto-refresh the data.

Type Selection Reference

Core Features

Columnar Storage: Uses Parquet format by default, reading only the necessary columns during queries, suitable for large-scale analysis.

Primary Key Support: After defining a primary key, the system automatically deduplicates by primary key during real-time writes, suitable for CDC sync scenarios.

Time Travel: Retains historical version data, supporting queries of data states at historical points in time.

Partitioning and Clustering: Optimizes query performance through partition pruning and clustered joins.

Quick Example

-- Create a table
CREATE TABLE orders (
    order_id    BIGINT       PRIMARY KEY,
    user_id     BIGINT       NOT NULL,
    amount      DECIMAL(10,2),
    created_at  TIMESTAMP
)
PARTITION BY (DATE(created_at));

-- Insert data
INSERT INTO orders VALUES (1, 101, 99.00, NOW());

Related Documentation

• CREATE TABLE — Table creation syntax
• ALTER TABLE — Modify table structure
• Partitioning — Partition design guide
• Clustering — Clustering design guide
• Time Travel — Historical data queries