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  1. Overview
    • Snowflake Horizon Catalog
      • Applications and tools for connecting to Snowflake
      • Virtual warehouses
      • Databases, Tables, & Views
      • Data types
        • Data Integration
          1. Snowflake Openflow
          2. Apache Iceberg™
            1. Apache Iceberg™ Tables
            2. Snowflake Open Catalog
        • Data engineering
          1. Data loading
          2. Dynamic tables
          3. Streams and tasks
          4. Row timestamps
            • DCM Projects
            • dbt Projects on Snowflake
            • Data Unloading
          5. Storage lifecycle policies
          6. Migrations
          7. Queries
          8. Listings
          9. Collaboration
          10. Snowflake AI & ML
          11. Snowflake Postgres
          12. Alerts & Notifications
          13. Security
          14. Data Governance
          15. Privacy
          16. Organizations & Accounts
          17. Business continuity & data recovery
          18. Performance optimization
          19. Cost & Billing

          Databases, Tables & Views¶

          All data in Snowflake is maintained in databases. Each database consists of one or more schemas, which are logical groupings of database objects, such as tables and views. Snowflake does not place any hard limits on the number of databases, schemas (within a database), or objects (within a schema) you can create.

          Next Topics:

          • Understanding Snowflake Table Structures
          • Working with Temporary and Transient Tables
          • Introduction to external tables
          • Search optimization service
          • Overview of Views
          • Working with Secure Views
          • Working with Materialized Views
          • Table Design Considerations
          • Cloning considerations
          • Data storage considerations

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          1. Data Definition Language (DDL) commands