feat(pipeline): add Meltano + dbt + Airflow ELT pipeline scaffold

Replaces the hand-rolled integrator with a production-grade ELT pipeline
using Meltano (Singer taps), dbt Core (medallion architecture), and
Apache Airflow (orchestration). Adds Typesense for search and PostGIS
for geospatial queries.

- 6 custom Singer taps (GIAS, EES, Ofsted, Parent View, FBIT, IDACI)
- dbt project: 12 staging, 5 intermediate, 12 mart models
- 3 Airflow DAGs (daily/monthly/annual schedules)
- Typesense sync + batch geocoding scripts
- docker-compose: add Airflow, Typesense; upgrade to PostGIS
- Portainer stack definition matching live deployment topology

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

pipeline/scripts/geocode_postcodes.py

@@ -0,0 +1,118 @@
+"""
+Batch geocode postcodes via Postcodes.io and update dim_location with lat/lng + PostGIS geometry.
+
+Usage:
+    python geocode_postcodes.py [--batch-size 100]
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import time
+
+import psycopg2
+import psycopg2.extras
+import requests
+
+POSTCODES_IO_BULK = "https://api.postcodes.io/postcodes"
+BATCH_SIZE = 100  # Postcodes.io max per request
+
+
+def get_db_connection():
+    return 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"),
+    )
+
+
+def fetch_ungeooded_postcodes(conn, limit: int = 5000) -> list[dict]:
+    """Get postcodes from dim_location that don't have lat/lng yet."""
+    with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
+        cur.execute("""
+            SELECT urn, postcode
+            FROM marts.dim_location
+            WHERE geom IS NULL
+              AND postcode IS NOT NULL
+            LIMIT %s
+        """, (limit,))
+        return cur.fetchall()
+
+
+def bulk_geocode(postcodes: list[str]) -> dict[str, tuple[float, float]]:
+    """Geocode a batch of postcodes via Postcodes.io bulk API."""
+    resp = requests.post(
+        POSTCODES_IO_BULK,
+        json={"postcodes": postcodes},
+        timeout=30,
+    )
+    resp.raise_for_status()
+
+    results = {}
+    for item in resp.json().get("result", []):
+        if item["result"]:
+            pc = item["query"].upper().replace(" ", "")
+            results[pc] = (item["result"]["latitude"], item["result"]["longitude"])
+    return results
+
+
+def update_locations(conn, updates: list[tuple[float, float, int]]):
+    """Update dim_location with lat/lng and PostGIS geometry."""
+    with conn.cursor() as cur:
+        psycopg2.extras.execute_batch(cur, """
+            UPDATE marts.dim_location
+            SET geom = ST_SetSRID(ST_MakePoint(%s, %s), 4326)
+            WHERE urn = %s
+        """, [(lng, lat, urn) for lat, lng, urn in updates])
+    conn.commit()
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Batch geocode school postcodes")
+    parser.add_argument("--batch-size", type=int, default=BATCH_SIZE)
+    args = parser.parse_args()
+
+    conn = get_db_connection()
+
+    rows = fetch_ungeooded_postcodes(conn)
+    if not rows:
+        print("All postcodes already geocoded.")
+        return
+
+    print(f"Geocoding {len(rows)} postcodes...")
+
+    total_updated = 0
+    for i in range(0, len(rows), args.batch_size):
+        batch = rows[i : i + args.batch_size]
+        postcodes = [r["postcode"] for r in batch if r["postcode"]]
+        urn_by_pc = {}
+        for r in batch:
+            if r["postcode"]:
+                pc_key = r["postcode"].upper().replace(" ", "")
+                urn_by_pc.setdefault(pc_key, []).append(r["urn"])
+
+        results = bulk_geocode(postcodes)
+
+        updates = []
+        for pc, (lat, lng) in results.items():
+            for urn in urn_by_pc.get(pc, []):
+                updates.append((lat, lng, urn))
+
+        if updates:
+            update_locations(conn, updates)
+            total_updated += len(updates)
+
+        print(f"  Batch {i // args.batch_size + 1}: geocoded {len(results)}/{len(postcodes)} postcodes")
+
+        # Rate limit: Postcodes.io is generous but be polite
+        time.sleep(0.2)
+
+    conn.close()
+    print(f"Done. Updated {total_updated} locations.")
+
+
+if __name__ == "__main__":
+    main()