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.
Split Kafka ingestion into a dedicated `kafka_load` DAG to enable independent
experimentation with data loading without triggering the full ETL pipeline.
Restructure implementation phases: Stage 1 uses `make data` for MVP, Stage 2
adds the standalone Kafka DAG, Stage 3 adds monitoring. Update DAG numbering,
parameters, task groups, and acceptance criteria to reflect the new
architecture.
Consolidate the orchestration strategy by merging `kafka_load` and
`etl_batch_transform` into a unified `etl_pipeline`. Replace BashOperator
dependencies on Kafka CLI with PythonOperators utilizing `kafka-python`.
Add detailed technical specifications for helper functions, MVP stages,
and validation checks to align with current infrastructure constraints.
Detail the architecture for migrating ETL orchestration from make to
Airflow. Define DAG structures for database initialization, Kafka data
ingestion, batch transformation, and quality monitoring. Include
technical specifications, operator details, and implementation phases.
ddadmin