Refine data flow diagrams and documentation to clarify error handling
in the ODS layer and partial data processing in the DDS layer. Add
detailed explanations for materialized views, batch SQL transformations,
and data quality metrics. Split DDS entity assembly diagrams for better
readability of event and click processing pipelines.
Add materialized views to capture parsing errors from browser, location,
device, and geo raw staging tables and route them to dedicated error
tables in the ODS layer. Refactor DDS refresh logic to handle partial
data arrivals where device and geo events may arrive independently by
using a unified click_id source with LEFT JOINs. Add TRUNCATE command
to prevent duplicate data accumulation in DQ summary table.
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.
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.
Add Apache Superset for data visualization to the docker-compose setup.
The custom Dockerfile installs additional tools and the clickhouse-connect
driver. The service is configured with health check, persistent volumes,
and environment variables.
Fix network definition name from 'ch_replicated' to 'cs_dwh' to match
service references. Comment out hardcoded container names to allow
Docker to generate unique names automatically and avoid conflicts.
- 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
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