Raw connector fields only become useful when the system understands what they mean. Metric Hive maps platform data into canonical entities, grains, dimensions, metrics, date rules, currency behavior, and table contracts so every dashboard, export, and analysis starts from the same decision-ready model.
1
Connect sources
Connectors read provider reports with their source scope, timestamps, currencies, identifiers, and coverage decisions, then preserve enough lineage for audit and troubleshooting.
2
Interpret meaning
The semantic contract decides each field's canonical entity, base grain, role, aggregation behavior, additivity, visibility, and export policy before users can query it.
3
Ship governed data
Dashboards, Explore, spreadsheets, BI, and data-warehouse exports all use the same contracts, so decisions are made from the same model wherever the data goes.
Why it matters
Metric Hive is built with the semantic layer at its core. It keeps connector ingestion, lake tables, query datasets, exports, and app surfaces aligned around the same business meaning.
Right decisions, fewer argumentsTeams compare fields that mean the same thing and can see when a metric is not safe to combine with another grain.
Not raw connector plumbingProvider fields are normalized into business concepts without losing source lineage, provider context, or controlled advanced detail.
Grain-aware metricsCounts, rates, snapshots, revenue, orders, and customer metrics carry aggregation rules so totals are not multiplied by unsafe joins.
Warehouse exports modeled and readyExports to destinations like BigQuery, Snowflake, spreadsheets, and BI tools use canonical tables instead of forcing your team to rebuild the model downstream.