- Data Explorer now hosts Business Overview, Federated (Trino),
Hadoop Lake and Data Dictionary as tabs instead of a separate
top-level view (richer, less stacked layout)
- TrinoFederationView supports an embedded mode driven by an
external active tab
- Remove the standalone Trino Federation sidebar entry/route
- Remove the Kibana shortcut from the sidebar (it already lives in
the Elasticsearch view)
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.
Add a dedicated business-data view (separate from infra/search):
- New "Data Explorer" tab: KPIs (customers, employees, products, source
row totals, sample revenue) + charts driven by Elasticsearch aggregations
(revenue over time, by region/channel/status, top customers & products,
HR by department/role/event, supply chain by type, telemetry averages),
with region + free-text filters.
- Backend /api/search/business endpoint: battery of ES aggregations with
numeric/date index template (so amount sums and date histograms work),
source totals via cheap planner estimates; biased the orders sample to
rows carrying customer names so people are searchable/visible.
Manual data entry on source systems:
- "Insert row" action in the Data Hub browser opens a column-aware form;
POST /api/sql/row/insert writes to Postgres/MySQL/Mongo/Cassandra/Neo4j.
- Inserts into CDC sources (PG/MySQL/Mongo) are captured by Debezium and
streamed to Kafka in real time; UI flags this and pulses the data flow.
New "Data Sources UI" tab with per-database Browser (catalog + sample data),
Query Console (SqlWorkbench) and embedded interactive Shell (DbShell via SSH).
Backend:
- Extend sql_console.py with Cassandra (CQL) + Neo4j (Cypher) engines
- Add GET /api/sql/catalog/{engine} and GET /api/sql/sample/{engine}
- ssh_terminal: optional initial_command for auto-launching DB CLIs
Frontend:
- DataSourcesView with 5-DB rail, health dots, Browser/Console/Shell sub-tabs
- DbShell embedded xterm terminal with docker exec CLI per engine
- Deep-link topology DB nodes to Data Sources UI (no SQL dock on platform)
- WorkbenchPanel restricted to agent mode only — frees dashboard space
Clicking agents opens a dedicated terminal panel; topology PostgreSQL/Trino nodes open SQL workbench with ten demo queries and Postgres vs Trino benchmark.
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