- Why:
- keep Airflow artifacts under a single airflow/ directory
- align repository layout with intended project structure
- What:
- move dags/ to airflow/dags/ and update compose mounts
- make SQL root resolution work in container and local runs
- update DAG path references in README, AGENTS, ARCHITECTURE, and plans
- remove tracked Python cache artifacts from old DAG location
- Check:
- airflow dags list
- airflow dags list-import-errors
- e2e success: ddl_init, kafka_load(limit=50), etl_pipeline
- Why:\n - User-facing docs mixed Airflow and legacy CLI ingest paths and caused confusion\n- What:\n - Rework README quick start and status to use DAG chain ddl_init -> kafka_load -> etl_pipeline\n - Rewrite runbook as canonical Airflow-first execution flow\n - Sync architecture diagrams/sequence and DQ wording with current SQL and DAG behavior\n- Check:\n - Verified updated sections and removed stale markers with rg in README.md, docs/ARCHITECTURE.md, plans/runbook.md
- Why:
- For DE task we only need full ingest or limit-based sample.
- load_* and full_load params were redundant and unclear in current flow.
- What:
- Remove full_load and load_* params from kafka_load DAG contract.
- Simplify kafka helpers (validate/check files) to fixed 4-stream ingest.
- Sync AGENTS, README, runbook, architecture and airflow plan docs.
- Check:
- python3 -m py_compile dags/kafka_load_dag.py dags/utils/kafka_helpers.py
- Airflow smoke/full runs: ddl_init -> kafka_load -> etl_pipeline (all success).
- Legacy path: make data && make transform (success).
Add persistent volume for ClickHouse to preserve data across container
restarts. The volume `clickhouse-data` is mounted to `/var/lib/clickhouse`,
ensuring data remains when containers are recreated.
Move DDL files from flat ddl/ directory to sql/ddl/ with layer-based
subdirectories (stg, ods, dds, dm). Move batch transformation SQL from
jobs/ to sql/ layer directories. Update scripts and documentation to
reflect new paths for improved organization and Airflow integration.
Update Airflow configuration to integrate with ClickHouse DWH instead of
PostgreSQL training database. Changes include:
- Switch Airflow dependencies from PostgreSQL to ClickHouse connector
- Update docker-compose to use ClickHouse connection and correct Dockerfile
- Refactor airflow/requirements.txt to include only essential packages
- Add DAGs directory for ETL pipeline orchestration
- Update documentation to reflect Airflow integration and access credentials
- Adjust service dependencies to wait for ClickHouse startup
Add batch ETL pipeline with ODS→DDS→DM transformation jobs and scripts.
Create DDL infrastructure with automated database schema application.
Update Makefile with transform target for executing batch processes.
Rewrite README with complete Russian documentation including architecture
diagrams, quick start guide, and data flow visualization.
ddadmin