feat: competitive intelligence & historical pattern matching layer

services/lake_publisher/iceberg.py

@@ -124,6 +124,27 @@ TABLE_SCHEMAS: dict[str, pa.Schema] = {
     "model_performance": MODEL_PERFORMANCE_SCHEMA,
 }
 
+# Lazily register schemas defined in worker.py to avoid circular imports.
+# These are added after the initial dict definition.
+def _register_worker_schemas() -> None:
+    from services.lake_publisher.worker import (
+        COMPETITOR_RELATIONSHIPS_SCHEMA,
+        COMPETITIVE_SIGNALS_SCHEMA,
+        GLOBAL_EVENTS_SCHEMA,
+        MACRO_IMPACTS_SCHEMA,
+        TREND_PROJECTIONS_SCHEMA,
+    )
+    TABLE_SCHEMAS["competitor_relationships"] = COMPETITOR_RELATIONSHIPS_SCHEMA
+    TABLE_SCHEMAS["competitive_signals"] = COMPETITIVE_SIGNALS_SCHEMA
+    TABLE_SCHEMAS["global_events"] = GLOBAL_EVENTS_SCHEMA
+    TABLE_SCHEMAS["macro_impacts"] = MACRO_IMPACTS_SCHEMA
+    TABLE_SCHEMAS["trend_projections"] = TREND_PROJECTIONS_SCHEMA
+
+try:
+    _register_worker_schemas()
+except ImportError:
+    pass  # worker.py not available in minimal test environments
+
 
 @dataclass(frozen=True)
 class IcebergTableDef: