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Schema: flow.nft Table: dim_moment_metadata Type: View

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Description

NFT moment metadata scraped from on-chain activity across multiple Flow NFT projects. This table captures metadata that is stored directly on the blockchain when moments are minted, providing a decentralized source of NFT information. The table supports multiple NFT collections including All Day, TopShot, and Golazos, with comprehensive metadata including play details, series information, and collection hierarchy.

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Key Use Cases

  • Analyze on-chain NFT metadata across multiple collections
  • Track moment creation and minting activity
  • Support cross-collection NFT analytics
  • Enable decentralized metadata verification
  • Power NFT explorer and discovery tools

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Important Relationships

  • Can be joined with nft__ez_nft_sales for sales analytics
  • Links to nft__dim_allday_metadata and nft__dim_topshot_metadata for enriched data
  • NFT collection and ID enable cross-table joins
  • Supports multiple NFT projects on Flow

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Commonly-used Fields

  • nft_collection: Contract address of the NFT collection
  • nft_id: Unique identifier for the NFT moment
  • serial_number: Edition number within the collection
  • set_name: Name of the set containing the moment
  • series_name: Series name for organizational hierarchy
  • metadata: JSON object containing detailed moment information
  • tier: Rarity tier classification

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Columns

Column NameData TypeDescription
NFT_COLLECTIONTEXTThe contract address or identifier for the NFT collection on the Flow blockchain. Data type: STRING. This field identifies the smart contract that manages the NFT collection, enabling collection-level analytics, filtering, and protocol attribution. Used for grouping NFTs by collection, tracking collection performance, and joining with metadata tables. Example: ‘A.e4cf4bdc1751c65d.AllDay’ for NFL All Day, ‘A.87ca73a41bb50ad5.TopShot’ for NBA TopShot. Important for collection analytics, marketplace filtering, and cross-collection comparisons.
NFT_IDTEXTThe 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_NUMBERTEXTThe 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.
MAX_MINT_SIZETEXTThe gross NFTs minted for the batch.
PLAY_IDTEXTThe numeric ID of the play for the moment. For example, with topshot moment ABC may be a version of Play 123. The play id is 123 and moment metadata is associated with the play, rather than the moment.
SERIES_IDTEXTThe numeric ID for the series of moments or plays. This corresponds with the series column found in some metadata tables.
SERIES_NAMETEXTThe series for this particular set of drops.
SET_IDTEXTThe numeric ID for the set. This corresponds with the set name found in some metadata columns.
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.
EDITION_IDTEXTThe numeric ID for the edition of moments. This is likely a bundle of moment NFTs within the same type of play, differentiated by some trait like rarity.
TIERTEXTThe tiered rarity of the edition.
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_MOMENT_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.