- 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.
mo