aptos.price Table: dim_asset_metadata Type: View
What
A comprehensive dimensional table holding asset metadata and other relevant details pertaining to each id, from multiple providers. This data set includes raw, non-transformed data coming directly from the provider APIs and rows are not intended to be unique. As a result, there may be data quality issues persisting in the APIs that flow through to this dimensional model. If you are interested in using a curated data set instead, please utilize ez_asset_metadata.Columns
| Column Name | Data Type | Description |
|---|---|---|
| TOKEN_ADDRESS | TEXT | The specific address representing the asset on a specific platform. This will be NULL if referring to a native asset. |
| ASSET_ID | TEXT | The unique identifier representing the asset. |
| SYMBOL | TEXT | The symbol of asset. |
| NAME | TEXT | The name of asset. |
| PROVIDER | TEXT | The provider or source of the data. |
| INSERTED_TIMESTAMP | TIMESTAMP_NTZ | The UTC timestamp when the row was inserted into the table, representing when the data was first recorded. Data type: Timestamp Example: 2024-01-15 14:30:25.123456 Business Context: Essential for data lineage tracking and insertion timing analysis. Critical for understanding data freshness and processing delays. Enables data quality analysis and processing performance monitoring. |
| MODIFIED_TIMESTAMP | TIMESTAMP_NTZ | The UTC timestamp when the row was last modified, representing when the data was most recently updated. Data type: Timestamp Example: 2024-01-15 14:30:25.123456 Business Context: Essential for data freshness analysis and update tracking. Critical for understanding data modification patterns and change frequency. Enables data quality monitoring and update performance analysis. |
| DIM_ASSET_METADATA_ID | TEXT | The unique primary key identifier for each row in the table, ensuring data integrity and uniqueness. Data type: String Example: 0x1234567890abcdef1234567890abcdef1234567890abcdef1234567890abcdef Business Context: Essential for data integrity and unique row identification. Critical for join operations and data relationship management. Enables precise data retrieval and referential integrity maintenance. |