Adds native Command Center features (no new containers) integrated as sub-tabs
in the existing Data Explorer and Data Quality views:
- Continuous Data Quality (dq_monitor.py): live completeness/uniqueness/validity/
freshness scorecards via Trino with rolling trends → DataQuality "Live Monitoring".
- Ownership & stewardship (catalog_governance.py): owner/steward/tier matrix,
orphan detection, business glossary; local store best-effort synced to
OpenMetadata (owner PATCH) → Data Explorer "Ownership".
- Access & policy posture: per-dataset compliance combining PII masking, ownership,
live DQ and observability alerts vs data contracts → Data Explorer "Access & Policies".
- Lineage (lineage.py): staged source→CDC→Spark→S3→Iceberg→Trino→serving graph with
live row counts and column-level PII/masking tracing → Data Explorer "Lineage".
- Observability (observability.py): volume/freshness/schema-drift monitoring with
alerts → Data Explorer "Observability".
- Shared lake_meta.py dataset registry + bounded Trino client; fast native row-count
and PK-indexed freshness so monitors stay cheap on 25-54M-row tables.
- LLM context (lab_context.py) enriched with DQ scores, ownership and active alerts.
Agent terminals were idle (one-shot probe) while agents were busy in the
background. Now every agent streams what it is actually doing:
- agent_terminal: emit_threadsafe() so background threads can stream lines.
- agent_ops: Data Custodian DML loop logs the real INSERT/UPDATE/DELETE SQL
(+ Mongo ops) and Hadoop-offload Trino CTAS/INSERT to its terminal; ETL
Guardian announces each orchestrated movement.
- movements: trigger_and_watch streams the Airflow DAG / API call, conf,
before/after Trino counts and result to the owning agent terminal.
- etl_offload: per-dataset read + pyarrow->S3 parquet writes and cycle
summaries stream to the ETL Guardian terminal.
- agent_activity (new): round-robin live probes for Lakehouse Ops, Hadoop
Ranger (NameNode JMX + YARN), Infra Sentinel (Dockhand inventory + host
load) and Network Watcher (VLAN 20/21 path checks).
- fix: YARN ResourceManager runs on 10.0.21.62:8088 (was .61).
- ui: terminal dock merges the selected agent ops stream with the node probe.
- etl_offload.py: autonomous agent backfills/tails source DBs (PG/MySQL/
Mongo/Cassandra) to S3 as Parquet in small chunks, accumulates a live
federated business matrix (/api/etl/status, /api/etl/business, /run, /config).
- storage_s3.py: buffer generated CDC + masked curated rows to S3, overlay
live last-write into analytics; put_object_bytes for Parquet parts.
- trino_federated.py: capture generated rows + archive to S3; generator_active.
- dataflow.py: pulse generate + kafka/spark->S3 archive edges when active.
- StorageView: realtime ETL ingest panel; TrinoFederationView: realtime
business KPIs/charts from /api/etl/business.
- ChangesView: top KPIs/charts now overlay the live WS stream on server stats
so they update in lock-step with the bottom feed; faster 2.5s refresh.
- useCommandCenter: retain 800 live CDC changes.
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.
- DAG hadoop_to_trino (deployed to Airflow) + worker hadoop_to_trino.py (on the
Hadoop master) move HDFS historical_sales -> iceberg.hadoop.historical_sales_hdfs.
- api/movements.py: movement registry, Airflow trigger+watch, run tracking
(state/duration/rows), endpoints /api/movements, /{id}/run, /runs.
- agent_ops.py: ETL-agent loop autonomously triggers movements on an interval
and logs each run; /api/agent-ops/etl/toggle + etl status.
Add api/cdc_consumer.py: aiokafka background consumer subscribes to the CDC
topics (postgres_sales/mysql_hr/mongodb_supplychain + cassandra/neo4j), parses
Debezium before/after envelopes, keeps a ring buffer and publishes each change
live as type=cdc_change. Endpoints /api/changes, /api/changes/stats, /status.
Deploys Apache Hive 3.1.3 (Derby metastore + external table over the HDFS
historical CSV, MapReduce exec) on the Hadoop master, so the engine comparison
shows a REAL measured Hive latency (~5.2s) next to live Trino (~0.3s); Impala
stays clearly-labelled representative. The API re-measures Hive over SSH on a
30-min TTL (cached + persisted, with a committed seed). Adds filters
(year/region/category/channel), a region×category heatmap, Trino exec stats,
and a "where is this data read from" lineage panel (Trino->S3/Iceberg/Parquet
with snapshot+files, Hive->HDFS/CSV with namenode+files). Mounts host SSH key
read-only into the api container for the live Hive benchmark.
Adds an Analytics sub-tab to the Hadoop view with live KPIs and revenue
breakdowns (by year/region/category/channel) queried from Trino over
iceberg.hadoop.historical_sales, plus a query-engine comparison panel. Trino
latency is measured live; Impala and Hive are shown as clearly-labelled
representative figures (those engines are not deployed). New cached endpoints
/api/hadoop/analytics and /api/hadoop/engines.
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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