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.
Replace materialized view joins with batch SQL transformations to avoid
consistency issues with out-of-order data. Document the reasoning for
using batch processing for ODS to DDS layer, including handling of
eventual consistency and versioning in ReplacingMergeTree. Update
data flow diagrams and remove MV creation DDL for DDS tables. Add
documentation for error handling tables and batch transformation jobs.
Add comprehensive plan for migrating executable DDL statements from markdown
to separate SQL files organized by layer. The plan outlines artifact structure,
execution requirements via make/Airflow, and environment parameters.
Existing inline DDL content is now marked as legacy in an appendix section,
providing clear separation between planned implementation and current state.
Add build automation via Makefile with targets for docker compose
management, DDL application, and data ingestion. Implement a robust bash
script for loading JSONL demo data into Kafka topics with configurable
options for limits, full dataset loading, and topic reset behavior.
Update Kafka broker address to use internal Docker network port (29092)
instead of external port (9092). Add kafka_ingest_plan.md with detailed
implementation strategy and runbook.md with user instructions.
- Описаны слои STG/ODS/DDS/DM и связи потоков (`event_id`/`click_id`)
- Добавлены DDL и MV-пайплайн для ingestion из Kafka (ClickHouse) + типизация/дедуп/DQ
- Добавлены витрины/VIEW для BI (Superset), mermaid-диаграмма и операционные заметки
ddadmin