flow.core Table: dim_address_mapping Type: Base Table
What
Description
This table maps EVM addresses to Flow addresses based on COA (Custody of Account) Creation events. Each row represents an association between a Flow address and an EVM address, enabling cross-chain identity mapping and analytics. A single Flow address may have multiple EVM addresses linked to it, reflecting multi-chain participation or asset bridging. The table is updated as new COA Creation events are detected on-chain.Key Use Cases
- Linking user or contract activity across Flow and EVM-compatible chains
- Supporting cross-chain analytics, wallet attribution, and identity resolution
- Enabling DeFi, NFT, and bridge analytics that require address mapping
- Auditing and monitoring asset flows between Flow and EVM ecosystems
Important Relationships
- Can be joined to Flow transaction and event tables (e.g.,
core.fact_transactions,core.fact_events) viaFLOW_ADDRESSfor on-chain activity - Can be joined to EVM-based analytics tables via
EVM_ADDRESSfor cross-chain analysis - Used by curated models in DeFi and NFT domains to enrich user and contract analytics with cross-chain context
Commonly-used Fields
FLOW_ADDRESS: The Flow blockchain address for the user or contractEVM_ADDRESS: The associated EVM-compatible addressBLOCK_TIMESTAMP_ASSOCIATED: Timestamp when the mapping was establishedBLOCK_HEIGHT_ASSOCIATED: Block height at which the mapping was recorded
Columns
| Column Name | Data Type | Description |
|---|---|---|
| BLOCK_TIMESTAMP_ASSOCIATED | TIMESTAMP_NTZ | The timestamp (in UTC) when the block or transaction was recorded on the Flow blockchain. Data type: TIMESTAMPNTZ. This field is essential for time-series analysis, ordering events, and joining with other tables by time. For example, a block with blockheight 100,000 may have a block_timestamp of ‘2023-01-01 12:00:00’. Used for analytics on network activity, transaction throughput, and historical state reconstruction. |
| BLOCK_HEIGHT_ASSOCIATED | NUMBER | The block number, corresponds with height. |
| FLOW_ADDRESS | TEXT | The unique on-chain address representing an account or contract on the Flow blockchain. Data type: STRING. Addresses are used to identify participants, contracts, and assets in all Flow transactions and events. Example: ‘0x1cf0e2f2f715450’. Used for joins, analytics, and entity mapping. For more details, see Flow Accounts and Addresses. |
| EVM_ADDRESS | TEXT | The unique on-chain address representing an account or contract on the Flow blockchain. Data type: STRING. Addresses are used to identify participants, contracts, and assets in all Flow transactions and events. Example: ‘0x1cf0e2f2f715450’. Used for joins, analytics, and entity mapping. For more details, see Flow Accounts and Addresses. |
| DIM_ADDRESS_MAPPING_ID | TEXT | pk_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. |
| MODIFIED_TIMESTAMP | TIMESTAMP_NTZ | The 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. |
| INSERTED_TIMESTAMP | TIMESTAMP_NTZ | The 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. |