aptos.price Table: fact_prices_ohlc_hourly Type: View
What
A comprehensive fact table holding id and provider specific open, high, low, close hourly prices, 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 fact based model. If you are interested in using a curated data set instead, please utilize ez_prices_hourly.Columns
| Column Name | Data Type | Description |
|---|---|---|
| ASSET_ID | TEXT | The unique identifier representing the asset. |
| HOUR | TIMESTAMP_NTZ | Hour that the price was recorded at. |
| OPEN | FLOAT | Opening price of the recorded hour in USD. |
| HIGH | FLOAT | Highest price of the recorded hour in USD |
| LOW | FLOAT | Lowest price of the recorded hour in USD |
| CLOSE | FLOAT | Closing price of the recorded hour in USD |
| PROVIDER | TEXT | PROVIDER column |
| 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. |
| FACT_PRICES_OHLC_HOURLY_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. |