- Зачем:
- нужен основной ручной интерфейс стенда для backfill/import/check без консольной матрицы переменных.
- Что:
- добавлен DAG generator_control с параметрами Airflow, ветвлением операций и ожиданием ETL.
- вынесена общая логика запуска и предпроверок генератора для Airflow.
- обновлены compose-настройки, зависимости, тесты и документация по пульту.
- Проверка:
- uv run --with pytest --with-requirements generator/requirements.txt pytest generator/tests -q.
- docker compose config --quiet.
Add comprehensive DAG implementation for ClickHouse schema initialization
and ETL pipeline orchestration. The ddl_init_dag manages database schema
creation across stg/ods/dds/dm layers with verification capabilities. The
etl_pipeline_dag implements full ODS to DDS to DM transformation flow with
data quality checks, branching logic for full/incremental loads, and
timeout handling for data availability.
Additional changes:
- Upgrade Airflow from 2.9.3 to 2.10.5
- Fix ClickHouse connection to use native protocol port 9000
- Mount SQL directory in docker-compose for DAG execution
- Update project requirements and documentation comments
- Remove unused pandas dependency
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 Apache Airflow infrastructure with webserver, scheduler, and metadata
database to enable DAG-based pipeline orchestration. Includes optimized
requirements file and Docker configuration for Airflow 2.9.3.
ddadmin