diff --git a/.semversioner/next-release/patch-20260223133523034773.json b/.semversioner/next-release/patch-20260223133523034773.json
new file mode 100644
index 0000000000..67ae560b15
--- /dev/null
+++ b/.semversioner/next-release/patch-20260223133523034773.json
@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "create_final_documents streaming"
+}
diff --git a/packages/graphrag/graphrag/index/workflows/create_final_documents.py b/packages/graphrag/graphrag/index/workflows/create_final_documents.py
index ccbd967821..7b3f65f991 100644
--- a/packages/graphrag/graphrag/index/workflows/create_final_documents.py
+++ b/packages/graphrag/graphrag/index/workflows/create_final_documents.py
@@ -1,14 +1,17 @@
-# Copyright (c) 2024 Microsoft Corporation.
+# Copyright (C) 2026 Microsoft
 # Licensed under the MIT License
 
-"""A module containing run_workflow method definition."""
+"""Workflow to create final documents with text unit mappings."""
 
 import logging
+from typing import Any
 
-import pandas as pd
+from graphrag_storage.tables.table import Table
 
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.data_model.data_reader import DataReader
+from graphrag.data_model.row_transformers import (
+    transform_document_row,
+)
 from graphrag.data_model.schemas import DOCUMENTS_FINAL_COLUMNS
 from graphrag.index.typing.context import PipelineRunContext
 from graphrag.index.typing.workflow import WorkflowFunctionOutput
@@ -20,49 +23,51 @@ async def run_workflow(
     _config: GraphRagConfig,
     context: PipelineRunContext,
 ) -> WorkflowFunctionOutput:
-    """All the steps to transform final documents."""
+    """Transform final documents via streaming Table reads/writes."""
     logger.info("Workflow started: create_final_documents")
-    reader = DataReader(context.output_table_provider)
-    documents = await reader.documents()
-    text_units = await reader.text_units()
 
-    output = create_final_documents(documents, text_units)
-
-    await context.output_table_provider.write_dataframe("documents", output)
+    async with (
+        context.output_table_provider.open(
+            "text_units",
+        ) as text_units_table,
+        context.output_table_provider.open(
+            "documents",
+            transformer=transform_document_row,
+        ) as documents_table,
+        context.output_table_provider.open(
+            "documents",
+        ) as output_table,
+    ):
+        sample = await create_final_documents(
+            text_units_table,
+            documents_table,
+            output_table,
+        )
 
     logger.info("Workflow completed: create_final_documents")
-    return WorkflowFunctionOutput(result=output)
-
-
-def create_final_documents(
-    documents: pd.DataFrame, text_units: pd.DataFrame
-) -> pd.DataFrame:
-    """All the steps to transform final documents."""
-    renamed = text_units.loc[:, ["id", "document_id", "text"]].rename(
-        columns={
-            "document_id": "chunk_doc_id",
-            "id": "chunk_id",
-            "text": "chunk_text",
-        }
-    )
-
-    joined = renamed.merge(
-        documents,
-        left_on="chunk_doc_id",
-        right_on="id",
-        how="inner",
-        copy=False,
-    )
-
-    docs_with_text_units = joined.groupby("id", sort=False).agg(
-        text_unit_ids=("chunk_id", list)
-    )
-
-    rejoined = docs_with_text_units.merge(
-        documents,
-        on="id",
-        how="right",
-        copy=False,
-    ).reset_index(drop=True)
-
-    return rejoined.loc[:, DOCUMENTS_FINAL_COLUMNS]
+    return WorkflowFunctionOutput(result=sample)
+
+
+async def create_final_documents(
+    text_units_table: Table,
+    documents_table: Table,
+    output_table: Table,
+) -> list[dict[str, Any]]:
+    """Build text-unit mapping, then stream-enrich documents."""
+    mapping: dict[str, list[str]] = {}
+    async for row in text_units_table:
+        document_id = row.get("document_id", "")
+        if document_id:
+            mapping.setdefault(document_id, []).append(
+                row["id"],
+            )
+
+    sample_rows: list[dict[str, Any]] = []
+    async for row in documents_table:
+        row["text_unit_ids"] = mapping.get(row["id"], [])
+        out = {c: row.get(c) for c in DOCUMENTS_FINAL_COLUMNS}
+        await output_table.write(out)
+        if len(sample_rows) < 5:
+            sample_rows.append(out)
+
+    return sample_rows