school_compare/pipeline/dags/school_data_pipeline.py

"""
School Data Pipeline — Airflow DAG

Orchestrates the full ELT pipeline:
  Extract (Meltano) → Validate → Transform (dbt) → Geocode → Sync Typesense → Invalidate Cache

Schedule:
  - GIAS: Daily at 03:00
  - Ofsted: 1st of month at 02:00
  - EES datasets: Annual (triggered manually or on detected release)
"""

from __future__ import annotations

from datetime import datetime, timedelta

from airflow import DAG
from airflow.utils.task_group import TaskGroup

try:
    from airflow.providers.standard.operators.bash import BashOperator
except ImportError:
    from airflow.operators.bash import BashOperator

PIPELINE_DIR = "/opt/pipeline"
MELTANO_BIN = "meltano"
DBT_BIN = "dbt"

default_args = {
    "owner": "school-compare",
    "depends_on_past": False,
    "email_on_failure": False,
    "retries": 1,
    "retry_delay": timedelta(minutes=5),
}


# ── Daily DAG (GIAS + downstream) ──────────────────────────────────────

with DAG(
    dag_id="school_data_daily",
    default_args=default_args,
    description="Daily school data pipeline (GIAS extract → full transform)",
    schedule="0 3 * * *",
    start_date=datetime(2025, 1, 1),
    catchup=False,
    tags=["school-compare", "daily"],
) as daily_dag:

    with TaskGroup("extract") as extract_group:
        extract_gias = BashOperator(
            task_id="extract_gias",
            bash_command=f"cd {PIPELINE_DIR} && {MELTANO_BIN} elt tap-uk-gias target-postgres",
        )

    validate_raw = BashOperator(
        task_id="validate_raw",
        bash_command=f"""
            cd {PIPELINE_DIR} && python -c "
import psycopg2, os, sys
conn = psycopg2.connect(
    host=os.environ.get('PG_HOST', 'localhost'),
    port=os.environ.get('PG_PORT', '5432'),
    user=os.environ.get('PG_USER', 'postgres'),
    password=os.environ.get('PG_PASSWORD', 'postgres'),
    dbname=os.environ.get('PG_DATABASE', 'school_compare'),
)
cur = conn.cursor()
cur.execute('SELECT count(*) FROM raw.gias_establishments')
count = cur.fetchone()[0]
conn.close()
if count < 20000:
    print(f'WARN: GIAS only has {{count}} rows, expected 60k+', file=sys.stderr)
    sys.exit(1)
print(f'Validation passed: {{count}} GIAS rows')
"
        """,
    )

    dbt_build = BashOperator(
        task_id="dbt_build",
        bash_command=f"cd {PIPELINE_DIR}/transform && {DBT_BIN} build --profiles-dir . --target production",
    )

    dbt_test = BashOperator(
        task_id="dbt_test",
        bash_command=f"cd {PIPELINE_DIR}/transform && {DBT_BIN} test --profiles-dir . --target production",
    )

    geocode_new = BashOperator(
        task_id="geocode_new",
        bash_command=f"cd {PIPELINE_DIR} && python scripts/geocode_postcodes.py",
    )

    sync_typesense = BashOperator(
        task_id="sync_typesense",
        bash_command=f"cd {PIPELINE_DIR} && python scripts/sync_typesense.py",
    )

    extract_group >> validate_raw >> dbt_build >> dbt_test >> geocode_new >> sync_typesense


# ── Monthly DAG (Ofsted) ───────────────────────────────────────────────

with DAG(
    dag_id="school_data_monthly_ofsted",
    default_args=default_args,
    description="Monthly Ofsted MI extraction and transform",
    schedule="0 2 1 * *",
    start_date=datetime(2025, 1, 1),
    catchup=False,
    tags=["school-compare", "monthly"],
) as monthly_ofsted_dag:

    extract_ofsted = BashOperator(
        task_id="extract_ofsted",
        bash_command=f"cd {PIPELINE_DIR} && {MELTANO_BIN} elt tap-uk-ofsted target-postgres",
    )

    dbt_build_ofsted = BashOperator(
        task_id="dbt_build",
        bash_command=f"cd {PIPELINE_DIR}/transform && {DBT_BIN} build --profiles-dir . --target production --select stg_ofsted_inspections+ int_ofsted_latest+ fact_ofsted_inspection+ dim_school+",
    )

    sync_typesense_ofsted = BashOperator(
        task_id="sync_typesense",
        bash_command=f"cd {PIPELINE_DIR} && python scripts/sync_typesense.py",
    )

    extract_ofsted >> dbt_build_ofsted >> sync_typesense_ofsted


# ── Annual DAG (EES: KS2, KS4, Census, Admissions, Phonics) ───────────

with DAG(
    dag_id="school_data_annual_ees",
    default_args=default_args,
    description="Annual EES data extraction (KS2, KS4, Census, Admissions, Phonics)",
    schedule=None,  # Triggered manually when new releases are published
    start_date=datetime(2025, 1, 1),
    catchup=False,
    tags=["school-compare", "annual"],
) as annual_ees_dag:

    with TaskGroup("extract_ees") as extract_ees_group:
        extract_ees = BashOperator(
            task_id="extract_ees",
            bash_command=f"cd {PIPELINE_DIR} && {MELTANO_BIN} elt tap-uk-ees target-postgres",
        )

    dbt_build_ees = BashOperator(
        task_id="dbt_build",
        bash_command=f"cd {PIPELINE_DIR}/transform && {DBT_BIN} build --profiles-dir . --target production",
    )

    dbt_test_ees = BashOperator(
        task_id="dbt_test",
        bash_command=f"cd {PIPELINE_DIR}/transform && {DBT_BIN} test --profiles-dir . --target production",
    )

    sync_typesense_ees = BashOperator(
        task_id="sync_typesense",
        bash_command=f"cd {PIPELINE_DIR} && python scripts/sync_typesense.py",
    )

    extract_ees_group >> dbt_build_ees >> dbt_test_ees >> sync_typesense_ees