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Schema: flow.nft Table: dim_ufc_strike_metadata Type: View

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Description

Metadata for UFC Strike NFTs, capturing information about UFC-themed digital collectibles on the Flow blockchain. This table provides comprehensive metadata for UFC Strike moments, including set information, listing details, and rich content data. The table supports UFC Strike marketplace integration and enables analysis of UFC-themed NFT performance and market activity.

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Key Use Cases

  • Analyze UFC Strike NFT performance and market activity
  • Track UFC-themed digital collectible analytics
  • Power UFC Strike dashboards and user-facing applications
  • Support content discovery and moment exploration
  • Enable UFC event correlation with NFT values

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Important Relationships

  • Can be joined with nft__ez_nft_sales for sales analytics
  • Links to other NFT metadata tables for cross-collection analysis
  • UFC Strike ID enables direct marketplace integration
  • Supports UFC-themed NFT ecosystem analysis

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Commonly-used Fields

  • nft_id: Unique identifier for the UFC Strike moment
  • serial_number: Edition number within the collection
  • listing_id: Marketplace listing identifier
  • set_name: Name of the UFC Strike set
  • set_description: Detailed description of the set
  • metadata: JSON object containing detailed moment information

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Columns

Column NameData TypeDescription
NFT_IDNUMBERThe unique identifier for the NFT within its collection. Data type: STRING or NUMBER. This field uniquely identifies a specific NFT token within a collection, typically representing the token ID or serial number assigned during minting. Used for NFT-level analytics, tracking individual token movements, and joining with metadata tables. Example: ‘12345’ for TopShot moment #12345, or ‘A.1234567890abcdef.Moment#456’ for a specific moment. Important for NFT tracking, sales analysis, and metadata joins.
SERIAL_NUMBERNUMBERThe serial number or edition number of the NFT within its collection. Data type: NUMBER. This field represents the specific edition number of the NFT, indicating its position within the total circulation of that particular moment or token. Used for rarity analysis, edition tracking, and determining the uniqueness of NFTs within a collection. Example: 25 for the 25th edition of a moment, 1 for the first edition. Important for rarity calculations, edition-based analytics, and determining NFT scarcity within collections.
LISTING_IDNUMBERThe listing ID used by the NFT marketplace.
SET_NAMETEXTThe name of the NFT set or series in which the moment was released. Data type: STRING. This field identifies the specific set or collection within a broader NFT project, enabling set-level analytics, rarity analysis, and set performance tracking. Used for set attribution, set-based market analysis, and understanding the organizational structure of NFT collections. Example: ‘Base Set’ for TopShot, ‘Genesis’ for All Day. Important for set-based analytics, rarity calculations, and understanding the hierarchical structure of NFT collections.
SET_DESCRIPTIONTEXT
METADATAOBJECTA JSON object containing comprehensive metadata for the NFT moment or play. Data type: OBJECT/VARIANT. This field stores detailed information about the NFT including play details, statistics, video URLs, and other descriptive data. Used for rich NFT analysis, content discovery, and detailed moment information retrieval. Example: Contains play statistics, video links, game context, and other moment-specific details. Important for comprehensive NFT analysis, content discovery, and providing rich context for NFT transactions and analytics.
DIM_UFC_STRIKE_METADATA_IDTEXTpk_id is a surrogate primary key, uniquely generated for each row in the table. Data type: STRING or INTEGER (implementation-specific). This field ensures every record is uniquely identifiable, even if the source data lacks a natural primary key. Used for efficient joins, deduplication, and as a reference in downstream models. Example: an auto-incremented integer or a UUID string. Essential for maintaining data integrity and supporting dbt tests for uniqueness.
INSERTED_TIMESTAMPTIMESTAMP_NTZThe UTC timestamp when the record was first created and inserted into this table. Data type: TIMESTAMP_NTZ. Used for ETL auditing, tracking data freshness, and identifying when data was loaded or updated in the analytics pipeline. Example: ‘2023-01-01 12:00:00’. This field is critical for monitoring data latency, troubleshooting ETL issues, and supporting recency tests in dbt.
MODIFIED_TIMESTAMPTIMESTAMP_NTZThe UTC timestamp when this record was last updated or modified by an internal ETL or dbt process. Data type: TIMESTAMP_NTZ. Used for change tracking, ETL auditing, and identifying the most recent update to a record. Example: ‘2023-01-02 15:30:00’. This field is important for troubleshooting data issues, monitoring pipeline health, and supporting recency or freshness tests in dbt.