feat(pii): masking for Cassandra & Neo4j (graph nodes + live property read) + MongoDB payload/free-text

api/dataflow.py

@@ -34,9 +34,11 @@ NODES: list[dict[str, Any]] = [
     {"id": "generator", "label": "Data Generator", "sub": "Airflow DAGs", "kind": "generator", "x": 9, "y": 34},
     {"id": "hdfs", "label": "Hadoop HDFS", "sub": "historical_sales", "kind": "hadoop", "x": 9, "y": 80},
     # col 1 — source databases
-    {"id": "postgres", "label": "PostgreSQL", "sub": "sales_orders", "kind": "source", "x": 28, "y": 16},
+    {"id": "postgres", "label": "PostgreSQL", "sub": "sales_orders", "kind": "source", "x": 28, "y": 12},
-    {"id": "mysql", "label": "MySQL", "sub": "employee_events", "kind": "source", "x": 28, "y": 38},
+    {"id": "mysql", "label": "MySQL", "sub": "employee_events", "kind": "source", "x": 28, "y": 28},
-    {"id": "mongodb", "label": "MongoDB", "sub": "events", "kind": "source", "x": 28, "y": 60},
+    {"id": "mongodb", "label": "MongoDB", "sub": "events", "kind": "source", "x": 28, "y": 44},
+    {"id": "cassandra", "label": "Cassandra", "sub": "device_metrics", "kind": "source", "x": 28, "y": 60},
+    {"id": "neo4j", "label": "Neo4j", "sub": "Product · Supplier graph", "kind": "source", "x": 28, "y": 76},
     # col 2 — change data capture
     {"id": "kafka", "label": "Kafka · Debezium", "sub": "CDC topics", "kind": "stream", "x": 47, "y": 24},
     {"id": "spark", "label": "Apache Spark", "sub": "Streaming · batch", "kind": "compute", "x": 62, "y": 38,
@@ -57,9 +59,13 @@ EDGES: list[dict[str, Any]] = [
     {"from": "generator", "to": "postgres", "kind": "generate", "movement_id": "gen_postgres"},
     {"from": "generator", "to": "mysql", "kind": "generate", "movement_id": "gen_mysql"},
     {"from": "generator", "to": "mongodb", "kind": "generate", "movement_id": "gen_mongodb"},
+    {"from": "generator", "to": "cassandra", "kind": "generate"},
+    {"from": "generator", "to": "neo4j", "kind": "generate"},
     {"from": "postgres", "to": "kafka", "kind": "cdc"},
     {"from": "mysql", "to": "kafka", "kind": "cdc"},
     {"from": "mongodb", "to": "kafka", "kind": "cdc"},
+    {"from": "cassandra", "to": "kafka", "kind": "cdc"},
+    {"from": "neo4j", "to": "kafka", "kind": "cdc"},
     {"from": "kafka", "to": "spark", "kind": "stream"},
     {"from": "spark", "to": "iceberg_curated", "kind": "movement", "movement_id": "spark_to_curated"},
     {"from": "spark", "to": "s3_cdc", "kind": "movement", "movement_id": "spark_to_s3"},
@@ -77,6 +83,8 @@ EDGES: list[dict[str, Any]] = [
     {"from": "postgres", "to": "openmetadata", "kind": "catalog"},
     {"from": "mysql", "to": "openmetadata", "kind": "catalog"},
     {"from": "mongodb", "to": "openmetadata", "kind": "catalog"},
+    {"from": "cassandra", "to": "openmetadata", "kind": "catalog"},
+    {"from": "neo4j", "to": "openmetadata", "kind": "catalog"},
     {"from": "trino", "to": "openmetadata", "kind": "catalog"},
 ]
 
@@ -165,7 +173,7 @@ async def _build() -> dict[str, Any]:
             used = spark.get("cores_used") or 0
             metric = f"{spark.get('alive_workers', 0)} workers · {used}/{cores} cores · {apps} apps"
             node["level"] = "ok" if spark.get("ui_ok") and (spark.get("status") or "").upper() == "ALIVE" else "warn"
-        elif n["id"] in ("postgres", "mysql", "mongodb"):
+        elif n["id"] in ("postgres", "mysql", "mongodb", "cassandra", "neo4j"):
             metric = f"{cdc.get('by_source', {}).get(n['id'], 0)} CDC/15m"
         elif n["id"] == "iceberg_hadoop":
             c = _iceberg_hadoop_count()

api/pii_catalog.py

@@ -62,6 +62,14 @@ DATASETS = [
      "table": "iceberg.hadoop.historical_sales_hdfs", "table_name": "historical_sales_hdfs", "catalog": "iceberg",
      "schema": "hadoop",
      "om_fqn": "atc_trino.iceberg.hadoop.historical_sales_hdfs"},
+    {"key": "cassandra", "node_id": "cassandra", "label": "Cassandra device_metrics",
+     "table": "cassandra_telemetry.telemetry.device_metrics", "table_name": "device_metrics",
+     "catalog": "cassandra_telemetry", "schema": "telemetry",
+     "om_fqn": "atc_trino.cassandra_telemetry.telemetry.device_metrics",
+     "native": {"engine": "cassandra"}},
+    {"key": "neo4j", "node_id": "neo4j", "label": "Neo4j Product/Supplier graph",
+     "table": "neo4j_graph", "table_name": "graph", "catalog": "neo4j",
+     "native": {"engine": "neo4j"}},
 ]
 
 KEY_BY_NODE = {ds["node_id"]: ds["key"] for ds in DATASETS}
@@ -112,7 +120,8 @@ PII_RULES: list[tuple[str, str]] = [
     (r"address|street|city|zip|postal|postcode", "ADDRESS"),
     (r"dob|birth|date_of_birth", "DOB"),
     (r"ip_addr|ip_address|_ip$|customer_ip|client_ip", "IP"),
-    (r"customer_id|client_id|user_id|account_id|member_id|device_id|subscriber_id|employee_id|guest_id|person_id", "IDENTIFIER"),
+    (r"customer_id|client_id|user_id|account_id|member_id|device_id|subscriber_id|employee_id|guest_id|person_id|supplier_id", "IDENTIFIER"),
+    (r"^payload$|payload|^notes$|^note$|free_text|freeform|raw_json|description", "FREEFORM"),
 ]
 
 _cache: dict[str, Any] = {"ts": 0.0, "data": None}
@@ -182,12 +191,29 @@ def _trino_columns(catalog: str, schema: str | None, table_name: str) -> list[st
         return []
 
 
+def _neo4j_property_keys() -> list[str]:
+    """Property keys across the Neo4j graph, used as the 'columns' for PII tagging."""
+    try:
+        from neo4j import GraphDatabase
+        uri = os.getenv("NEO4J_URI", f"bolt://{os.getenv('DB_HOST', '10.0.21.51')}:7687")
+        drv = GraphDatabase.driver(uri, auth=(os.getenv("NEO4J_USER", "neo4j"), os.getenv("NEO4J_PASSWORD", "testpwd")))
+        with drv.session() as s:
+            keys = [r["propertyKey"] for r in s.run("CALL db.propertyKeys() YIELD propertyKey RETURN propertyKey")]
+        drv.close()
+        return keys
+    except Exception:
+        return []
+
+
 def _build() -> dict[str, Any]:
     datasets_out = []
     total_pii = 0
     total_masked = 0
     om_used = False
     for ds in DATASETS:
+        if (ds.get("native") or {}).get("engine") == "neo4j":
+            cols = _neo4j_property_keys()
+        else:
             cols = _trino_columns(ds["catalog"], ds.get("schema"), ds["table_name"])
         om_tags = _om_column_tags(ds["om_fqn"]) if ds.get("om_fqn") else {}
         if om_tags: