docs(dags): обновлена документация для csv_to_postgres пайплайна

- Зачем:
  - отражение изменений после миграции с Greenplum на PostgreSQL
  - добавление описания новых DAG-ов для обучения
- Что:
  - удалено устаревшее упоминание Greenplum в educational-setup-plan.md
  - добавлено описание csv_to_postgres.py в educational-setup-plan.md
  - добавлено описание csv_to_postgres.py и csv_to_postgres_dq.py в dag-specifications.md
  - обновлена нумерация DAG-ов в dag-specifications.md
- Проверка:
  - просмотр файлов dag-specifications.md и educational-setup-plan.md

airflow-docker/dag-specifications.md

@@ -89,7 +89,59 @@ id,name,department,salary
 3,Charlie,Sales,48000
 ```
 
-#### 2.2 data_processing_dag.py
+#### 2.2 csv_to_postgres.py
+**Learning Objectives:**
+- Load CSV data into PostgreSQL database
+- Implement data quality checks
+- Use XCom for passing file paths between tasks
+- Work with PostgreSQL connections in Airflow
+
+**Scenario:**
+Generate sample orders data as CSV, load it into PostgreSQL, and verify data quality.
+
+**Tasks:**
+- `create_orders_table`: Create public.orders table in PostgreSQL
+- `generate_csv`: Generate sample orders CSV file
+- `preview_csv`: Display first few rows of CSV
+- `load_csv_to_postgres`: Load CSV data into PostgreSQL using temporary table
+
+**Database Connection:** Uses `postgres_training` connection (auto-provisioned by init script).
+
+**Sample Data Structure:**
+```csv
+order_id,order_ts,customer_id,amount
+1,2023-10-01 10:30:00,101,1250.50
+2,2023-10-01 11:45:00,102,890.00
+3,2023-10-02 09:15:00,103,2100.75
+```
+
+**Data Quality Checks:** See `csv_to_postgres_dq.py` for automated validation.
+
+#### 2.3 csv_to_postgres_dq.py
+**Learning Objectives:**
+- Implement data quality validation in Airflow
+- Use Python functions for data checks
+- Handle data quality failures
+- Separate validation from main ETL pipeline
+
+**Scenario:**
+Run automated data quality checks on the public.orders table after CSV loading.
+
+**Tasks:**
+- `check_table_exists`: Verify public.orders table exists
+- `check_schema`: Validate table schema matches expected structure
+- `check_row_count`: Ensure table has data
+- `check_duplicates`: Verify no duplicate order_id values
+
+**Quality Checks:**
+- Table existence in public schema
+- Column names and data types (order_id, order_ts, customer_id, amount)
+- Minimum row count (> 0)
+- Unique order_id values (no duplicates)
+
+**Helper Functions:** Located in `dags/helpers/postgres.py`.
+
+#### 2.4 data_processing_dag.py
 **Learning Objectives:**
 - ETL pipeline concepts
 - Multiple data sources

airflow-docker/educational-setup-plan.md

@@ -4,7 +4,6 @@
 
 ### 1. Docker Compose Structure Problems
 - Duplicate `services:` sections in [`docker-compose.yml`](airflow-docker/docker-compose.yml:1,21)
-- Missing Greenplum service (referenced in dependencies but not defined)
 - Inconsistent container naming
 
 ### 2. Missing Directory Structure
@@ -85,6 +84,7 @@ Create `.env` file with all variables hardcoded:
 - `file_operations_dag.py` - CSV file processing
 
 **Level 2: Intermediate**
+- `csv_to_postgres.py` - CSV to PostgreSQL pipeline with data quality checks
 - `data_processing_dag.py` - ETL pipeline with multiple steps
 - `branching_dag.py` - Conditional task execution