feat: Trino federation + Hadoop external tables + LLM data catalog

Trino Federation tab (3 sub-views):
- Federated: catalog landscape + a single cross-source SQL that joins
  PostgreSQL + MySQL + MongoDB (region scorecard) — the federation proof,
  computed in the background and cached (large full scans take ~2 min).
- Hadoop Lake: all federated business data materialized as external Iceberg
  tables on HDFS (iceberg.hadoop.*_ext, ~120k rows each) with live, fast
  business analytics (revenue by region/channel, top customers, HR by
  department, supply by type, telemetry averages). Includes a one-click
  "rebuild external tables" job.
- Data Dictionary: every business table + column with masked / visible PII
  badges and categories.

Backend trino_federated.py: /catalogs, /marquee(+refresh), /lake,
/materialize(+status), /dictionary. Name-based PII detection flags raw PII
in derived/lake tables as visible vs physically-masked curated layer.

LLM context: platform_context now emits a full BUSINESS DATA CATALOG section
(tables, columns, types, source row counts, federated scorecard) with exact
per-column masked/visible status, so the assistant knows the data in detail
and what is masked vs not.

api/main.py

@@ -53,6 +53,7 @@ from dataflow import router as dataflow_router
 from streaming_ops import router as streaming_router
 from spark_workbench import router as spark_workbench_router
 from pii_catalog import router as pii_router
+from trino_federated import router as federated_router
 from ssh_terminal import ssh_session
 from node_registry import NODE_IDS, NODE_AGENT, NODE_REGISTRY, is_node_id
 from node_ops import build_node_detail, probe_node, run_node_probe_task
@@ -757,6 +758,7 @@ app.include_router(dataflow_router)
 app.include_router(streaming_router)
 app.include_router(spark_workbench_router)
 app.include_router(pii_router)
+app.include_router(federated_router)
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],