agg narative country list

dags/etl_food_security.py

@@ -1,8 +1,57 @@
+"""
+AIRFLOW DAG — ETL Food Security BigQuery
+Kimball Data Warehouse Architecture
+
+Schedule : Setiap 3 hari sekali (timedelta(days=3))
+Catchup  : False
+
+Kimball ETL Flow:
+    ┌──────────────────────────────────────────────────────────────────────────┐
+    │  BRONZE (Raw)       SILVER (Staging→Cleaned)   GOLD (DW → Analytical)   │
+    │                                                                          │
+    │  raw_fao       ─┐                              dim_country               │
+    │  raw_worldbank ─┼→ staging_integrated          dim_indicator             │
+    │  raw_unicef    ─┘        ↓                     dim_time                  │
+    │                   cleaned_integrated  ───────→ dim_source                │
+    │                                                dim_pillar                │
+    │                                                fact_food_security        │
+    │                                                      ↓                   │
+    │                                                analytical_food_security  │
+    │                                                      ↓                   │
+    │                                                agg_pillar_composite      │
+    │                                                agg_pillar_by_country     │
+    │                                                agg_framework_by_country  │
+    │                                                agg_framework_asean       │
+    │                                                                          │
+    │  AUDIT : etl_logs, etl_metadata (setiap layer)                           │
+    └──────────────────────────────────────────────────────────────────────────┘
+
+Task Order:
+    verify_bigquery_connection
+        → load_fao_to_bronze
+        → load_worldbank_to_bronze
+        → load_unicef_to_bronze
+        → staging_integration_to_silver
+        → cleaned_integration_to_silver
+        → dimensional_model_to_gold
+        → analytical_layer_to_gold
+        → aggregation_to_gold
+
+Scripts folder harus berisi:
+    - bigquery_raw_layer.py             (run_verify_connection, run_load_fao, ...)
+    - bigquery_cleaned_layer.py         (run_cleaned_integration)
+    - bigquery_dimensional_model.py     (run_dimensional_model)
+    - bigquery_analytical_layer.py      (run_analytical_layer)
+    - bigquery_analysis_aggregation.py  (run_aggregation)
+    - bigquery_config.py
+    - bigquery_helpers.py
+    - bigquery_datasource.py
+"""
+
 from airflow import DAG
 from airflow.operators.python import PythonOperator
 from datetime import datetime
 
-# Import fungsi dari folder scripts
 from scripts.bigquery_raw_layer import (
     run_verify_connection,
     run_load_fao,
@@ -10,13 +59,36 @@ from scripts.bigquery_raw_layer import (
     run_load_unicef,
     run_staging_integration,
 )
+from scripts.bigquery_cleaned_layer import (
+    run_cleaned_integration,
+)
+from scripts.bigquery_dimensional_model import (
+    run_dimensional_model,
+)
+from scripts.bigquery_analytical_layer import (
+    run_analytical_layer,
+)
+from scripts.bigquery_aggregate_layer import (
+    run_aggregation,
+)
+
+# DEFAULT ARGS
+
+default_args = {
+    'owner': 'data-engineering',
+    'email': ['d1041221004@student.untan.ac.id'],
+}
+
+# DAG DEFINITION
 
 with DAG(
     dag_id            = "etl_food_security_bigquery",
+    description       = "Kimball ETL: FAO, World Bank, UNICEF → BigQuery (Bronze → Silver → Gold)",
+    default_args      = default_args,
     start_date        = datetime(2026, 3, 1),
-    schedule_interval = "@daily",
+    schedule_interval = "0 0 */3 * *",
     catchup           = False,
-    tags              = ["food-security", "bigquery", "kimball"]
+    tags              = ["food-security", "bigquery", "kimball"],
 ) as dag:
 
     task_verify = PythonOperator(
@@ -44,4 +116,25 @@ with DAG(
         python_callable = run_staging_integration
     )
 
-    task_verify >> task_fao >> task_worldbank >> task_unicef >> task_staging
+    task_cleaned = PythonOperator(
+        task_id         = "cleaned_integration_to_silver",
+        python_callable = run_cleaned_integration
+    )
+
+    task_dimensional = PythonOperator(
+        task_id         = "dimensional_model_to_gold",
+        python_callable = run_dimensional_model
+    )
+
+    task_analytical = PythonOperator(
+        task_id         = "analytical_layer_to_gold",
+        python_callable = run_analytical_layer
+    )
+
+    task_aggregation = PythonOperator(
+        task_id         = "aggregation_to_gold",
+        python_callable = run_aggregation
+    )
+
+    
+    task_verify >> task_fao >> task_worldbank >> task_unicef >> task_staging >> task_cleaned >> task_dimensional >> task_analytical >> task_aggregation

dags/test_simple.py

@@ -0,0 +1,14 @@
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+from datetime import datetime
+
+def hello():
+    print("hello world")
+
+with DAG(
+    dag_id="test_simple",
+    start_date=datetime(2026, 3, 1),
+    schedule_interval="@daily",
+    catchup=False
+) as dag:
+    PythonOperator(task_id="hello", python_callable=hello)

docker-compose.yaml

@@ -19,13 +19,9 @@ services:
       - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
       - AIRFLOW__CORE__EXECUTOR=LocalExecutor
       - AIRFLOW__CORE__LOAD_EXAMPLES=False
-      - GOOGLE_APPLICATION_CREDENTIALS=/opt/airflow/secrets/food-security-asean-project-826a4d7b302a.json
     volumes:
-      - airflow_dags:/opt/airflow/dags
       - airflow_logs:/opt/airflow/logs
       - airflow_plugins:/opt/airflow/plugins
-      - ./scripts:/opt/airflow/scripts
-      - ./secrets:/opt/airflow/secrets:ro
     ports:
       - "8081:8080"
     command: bash -c "airflow db init && airflow webserver"
@@ -39,17 +35,12 @@ services:
       - PYTHONPATH=/opt/airflow
       - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
       - AIRFLOW__CORE__EXECUTOR=LocalExecutor
-      - GOOGLE_APPLICATION_CREDENTIALS=/opt/airflow/secrets/food-security-asean-project-826a4d7b302a.json
     volumes:
-      - airflow_dags:/opt/airflow/dags
       - airflow_logs:/opt/airflow/logs
       - airflow_plugins:/opt/airflow/plugins
-      - ./scripts:/opt/airflow/scripts
-      - ./secrets:/opt/airflow/secrets:ro
     command: scheduler
 
 volumes:
   postgres_data:
-  airflow_dags:
   airflow_logs:
   airflow_plugins:

scripts/bigquery_analytical_layer.py

@@ -0,0 +1,557 @@
+"""
+BIGQUERY ANALYTICAL LAYER - DATA FILTERING
+FIXED: analytical_food_security disimpan di fs_asean_gold (layer='gold')
+
+Filtering Order:
+1. Load data (single years only)
+2. Determine year boundaries (2013 - auto-detected end year)
+3. Filter complete indicators PER COUNTRY (auto-detect start year, no gaps)
+4. Filter countries with ALL pillars (FIXED SET)
+5. Filter indicators with consistent presence across FIXED countries
+6. Save analytical table (value only, normalisasi & direction handled downstream)
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import datetime
+import logging
+from typing import Dict, List
+import json
+import sys
+
+if hasattr(sys.stdout, 'reconfigure'):
+    sys.stdout.reconfigure(encoding='utf-8')
+
+from scripts.bigquery_config import get_bigquery_client, CONFIG, get_table_id
+from scripts.bigquery_helpers import (
+    log_update,
+    load_to_bigquery,
+    read_from_bigquery,
+    setup_logging,
+    truncate_table,
+    save_etl_metadata,
+)
+from google.cloud import bigquery
+
+
+# =============================================================================
+# ANALYTICAL LAYER CLASS
+# =============================================================================
+
+class AnalyticalLayerLoader:
+    """
+    Analytical Layer Loader for BigQuery - CORRECTED VERSION v4
+
+    Key Logic:
+    1. Complete per country (no gaps from start_year to end_year)
+    2. Filter countries with all pillars
+    3. Ensure indicators have consistent country count across all years
+    4. Save raw value only (normalisasi & direction handled downstream)
+
+    Output: analytical_food_security -> DW layer (Gold) -> fs_asean_gold
+    """
+
+    def __init__(self, client: bigquery.Client):
+        self.client = client
+        self.logger = logging.getLogger(self.__class__.__name__)
+        self.logger.propagate = False
+
+        self.df_clean     = None
+        self.df_indicator = None
+        self.df_country   = None
+        self.df_pillar    = None
+
+        self.selected_country_ids = None
+
+        self.start_year    = 2013
+        self.end_year      = None
+        self.baseline_year = 2023
+
+        self.pipeline_metadata = {
+            'source_class'      : self.__class__.__name__,
+            'start_time'        : None,
+            'end_time'          : None,
+            'duration_seconds'  : None,
+            'rows_fetched'      : 0,
+            'rows_transformed'  : 0,
+            'rows_loaded'       : 0,
+            'validation_metrics': {}
+        }
+
+        self.pipeline_start = None
+        self.pipeline_end   = None
+
+    def load_source_data(self):
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info("STEP 1: LOADING SOURCE DATA from fs_asean_gold")
+        self.logger.info("=" * 80)
+
+        try:
+            query = f"""
+            SELECT
+                f.country_id,
+                c.country_name,
+                f.indicator_id,
+                i.indicator_name,
+                i.direction,
+                f.pillar_id,
+                p.pillar_name,
+                f.time_id,
+                t.year,
+                t.start_year,
+                t.end_year,
+                t.is_year_range,
+                f.value,
+                f.source_id
+            FROM `{get_table_id('fact_food_security', layer='gold')}` f
+            JOIN `{get_table_id('dim_country',        layer='gold')}` c ON f.country_id   = c.country_id
+            JOIN `{get_table_id('dim_indicator',      layer='gold')}` i ON f.indicator_id = i.indicator_id
+            JOIN `{get_table_id('dim_pillar',         layer='gold')}` p ON f.pillar_id    = p.pillar_id
+            JOIN `{get_table_id('dim_time',           layer='gold')}` t ON f.time_id      = t.time_id
+            """
+
+            self.logger.info("Loading fact table with dimensions...")
+            self.df_clean = self.client.query(query).result().to_dataframe(create_bqstorage_client=False)
+            self.logger.info(f"  Loaded: {len(self.df_clean):,} rows")
+
+            if 'is_year_range' in self.df_clean.columns:
+                yr = self.df_clean['is_year_range'].value_counts()
+                self.logger.info(f"  Breakdown:")
+                self.logger.info(f"    Single years (is_year_range=False): {yr.get(False, 0):,}")
+                self.logger.info(f"    Year ranges  (is_year_range=True):  {yr.get(True,  0):,}")
+
+            self.df_indicator = read_from_bigquery(self.client, 'dim_indicator', layer='gold')
+            self.df_country   = read_from_bigquery(self.client, 'dim_country',   layer='gold')
+            self.df_pillar    = read_from_bigquery(self.client, 'dim_pillar',     layer='gold')
+
+            self.logger.info(f"  Indicators: {len(self.df_indicator)}")
+            self.logger.info(f"  Countries:  {len(self.df_country)}")
+            self.logger.info(f"  Pillars:    {len(self.df_pillar)}")
+
+            self.pipeline_metadata['rows_fetched'] = len(self.df_clean)
+            return True
+
+        except Exception as e:
+            self.logger.error(f"Error loading source data: {e}")
+            raise
+
+    def determine_year_boundaries(self):
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info("STEP 2: DETERMINE YEAR BOUNDARIES")
+        self.logger.info("=" * 80)
+
+        df_2023                  = self.df_clean[self.df_clean['year'] == self.baseline_year]
+        baseline_indicator_count = df_2023['indicator_id'].nunique()
+
+        self.logger.info(f"\nBaseline Year: {self.baseline_year}")
+        self.logger.info(f"Baseline Indicator Count: {baseline_indicator_count}")
+
+        years_sorted      = sorted(self.df_clean['year'].unique(), reverse=True)
+        selected_end_year = None
+
+        for year in years_sorted:
+            if year >= self.baseline_year:
+                df_year              = self.df_clean[self.df_clean['year'] == year]
+                year_indicator_count = df_year['indicator_id'].nunique()
+                status               = "OK" if year_indicator_count >= baseline_indicator_count else "X"
+                self.logger.info(f"  [{status}] Year {int(year)}: {year_indicator_count} indicators")
+                if year_indicator_count >= baseline_indicator_count and selected_end_year is None:
+                    selected_end_year = int(year)
+
+        if selected_end_year is None:
+            selected_end_year = self.baseline_year
+            self.logger.warning(f"  [!] No year found, using baseline: {selected_end_year}")
+        else:
+            self.logger.info(f"\n  [OK] Selected End Year: {selected_end_year}")
+
+        self.end_year  = selected_end_year
+        original_count = len(self.df_clean)
+
+        self.df_clean = self.df_clean[
+            (self.df_clean['year'] >= self.start_year) &
+            (self.df_clean['year'] <= self.end_year)
+        ].copy()
+
+        self.logger.info(f"\nFiltering {self.start_year}-{self.end_year}:")
+        self.logger.info(f"  Rows before: {original_count:,}")
+        self.logger.info(f"  Rows after:  {len(self.df_clean):,}")
+        return self.df_clean
+
+    def filter_complete_indicators_per_country(self):
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info("STEP 3: FILTER COMPLETE INDICATORS PER COUNTRY (NO GAPS)")
+        self.logger.info("=" * 80)
+
+        grouped = self.df_clean.groupby([
+            'country_id', 'country_name', 'indicator_id', 'indicator_name',
+            'pillar_id', 'pillar_name'
+        ])
+
+        valid_combinations   = []
+        removed_combinations = []
+
+        for (country_id, country_name, indicator_id, indicator_name,
+             pillar_id, pillar_name), group in grouped:
+
+            years_present   = sorted(group['year'].unique())
+            start_year      = int(min(years_present))
+            end_year_actual = int(max(years_present))
+            expected_years  = list(range(start_year, self.end_year + 1))
+            missing_years   = [y for y in expected_years if y not in years_present]
+            has_gap         = len(missing_years) > 0
+
+            is_complete = (
+                end_year_actual >= self.end_year and
+                not has_gap and
+                (self.end_year - start_year) >= 4
+            )
+
+            if is_complete:
+                valid_combinations.append({'country_id': country_id, 'indicator_id': indicator_id})
+            else:
+                reasons = []
+                if end_year_actual < self.end_year:
+                    reasons.append(f"ends {end_year_actual}")
+                if has_gap:
+                    gap_str = str(missing_years[:3])[1:-1]
+                    if len(missing_years) > 3:
+                        gap_str += "..."
+                    reasons.append(f"gap:{gap_str}")
+                if (self.end_year - start_year) < 4:
+                    reasons.append(f"span={self.end_year - start_year}")
+                removed_combinations.append({
+                    'country_name'  : country_name,
+                    'indicator_name': indicator_name,
+                    'reasons'       : ", ".join(reasons)
+                })
+
+        self.logger.info(f"\n  [+] Valid:   {len(valid_combinations):,}")
+        self.logger.info(f"  [-] Removed: {len(removed_combinations):,}")
+
+        df_valid       = pd.DataFrame(valid_combinations)
+        df_valid['key'] = df_valid['country_id'].astype(str) + '_' + df_valid['indicator_id'].astype(str)
+        self.df_clean['key'] = (self.df_clean['country_id'].astype(str) + '_' +
+                                self.df_clean['indicator_id'].astype(str))
+
+        original_count = len(self.df_clean)
+        self.df_clean  = self.df_clean[self.df_clean['key'].isin(df_valid['key'])].copy()
+        self.df_clean  = self.df_clean.drop('key', axis=1)
+
+        self.logger.info(f"\n  Rows before: {original_count:,}")
+        self.logger.info(f"  Rows after:  {len(self.df_clean):,}")
+        self.logger.info(f"  Countries:   {self.df_clean['country_id'].nunique()}")
+        self.logger.info(f"  Indicators:  {self.df_clean['indicator_id'].nunique()}")
+        return self.df_clean
+
+    def select_countries_with_all_pillars(self):
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info("STEP 4: SELECT COUNTRIES WITH ALL PILLARS (FIXED SET)")
+        self.logger.info("=" * 80)
+
+        total_pillars        = self.df_clean['pillar_id'].nunique()
+        country_pillar_count = self.df_clean.groupby(['country_id', 'country_name']).agg({
+            'pillar_id'   : 'nunique',
+            'indicator_id': 'nunique',
+            'year'        : lambda x: f"{int(x.min())}-{int(x.max())}"
+        }).reset_index()
+        country_pillar_count.columns = [
+            'country_id', 'country_name', 'pillar_count', 'indicator_count', 'year_range'
+        ]
+
+        for _, row in country_pillar_count.sort_values('pillar_count', ascending=False).iterrows():
+            status = "[+] KEEP" if row['pillar_count'] == total_pillars else "[-] REMOVE"
+            self.logger.info(
+                f"  {status:<12} {row['country_name']:25s} "
+                f"{row['pillar_count']}/{total_pillars} pillars"
+            )
+
+        selected_countries        = country_pillar_count[country_pillar_count['pillar_count'] == total_pillars]
+        self.selected_country_ids = selected_countries['country_id'].tolist()
+
+        self.logger.info(f"\n  FIXED SET: {len(self.selected_country_ids)} countries")
+
+        original_count = len(self.df_clean)
+        self.df_clean  = self.df_clean[self.df_clean['country_id'].isin(self.selected_country_ids)].copy()
+
+        self.logger.info(f"  Rows before: {original_count:,}")
+        self.logger.info(f"  Rows after:  {len(self.df_clean):,}")
+        return self.df_clean
+
+    def filter_indicators_consistent_across_fixed_countries(self):
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info("STEP 5: FILTER INDICATORS WITH CONSISTENT PRESENCE")
+        self.logger.info("=" * 80)
+
+        indicator_country_start = self.df_clean.groupby([
+            'indicator_id', 'indicator_name', 'country_id'
+        ])['year'].min().reset_index()
+        indicator_country_start.columns = ['indicator_id', 'indicator_name', 'country_id', 'start_year']
+
+        indicator_max_start = indicator_country_start.groupby([
+            'indicator_id', 'indicator_name'
+        ])['start_year'].max().reset_index()
+        indicator_max_start.columns = ['indicator_id', 'indicator_name', 'max_start_year']
+
+        valid_indicators   = []
+        removed_indicators = []
+
+        for _, ind_row in indicator_max_start.iterrows():
+            indicator_id   = ind_row['indicator_id']
+            indicator_name = ind_row['indicator_name']
+            max_start      = int(ind_row['max_start_year'])
+            span           = self.end_year - max_start
+
+            if span < 4:
+                removed_indicators.append({
+                    'indicator_name': indicator_name,
+                    'reason'        : f"span={span} < 4"
+                })
+                continue
+
+            expected_years     = list(range(max_start, self.end_year + 1))
+            ind_data           = self.df_clean[self.df_clean['indicator_id'] == indicator_id]
+            all_years_complete = True
+            problematic_years  = []
+
+            for year in expected_years:
+                country_count = ind_data[ind_data['year'] == year]['country_id'].nunique()
+                if country_count < len(self.selected_country_ids):
+                    all_years_complete = False
+                    problematic_years.append(f"{int(year)}({country_count})")
+
+            if all_years_complete:
+                valid_indicators.append(indicator_id)
+            else:
+                removed_indicators.append({
+                    'indicator_name': indicator_name,
+                    'reason'        : f"missing countries in years: {', '.join(problematic_years[:5])}"
+                })
+
+        self.logger.info(f"\n  [+] Valid:   {len(valid_indicators)}")
+        self.logger.info(f"  [-] Removed: {len(removed_indicators)}")
+
+        if not valid_indicators:
+            raise ValueError("No valid indicators found after filtering!")
+
+        original_count = len(self.df_clean)
+        self.df_clean  = self.df_clean[self.df_clean['indicator_id'].isin(valid_indicators)].copy()
+
+        self.df_clean = self.df_clean.merge(
+            indicator_max_start[['indicator_id', 'max_start_year']], on='indicator_id', how='left'
+        )
+        self.df_clean = self.df_clean[self.df_clean['year'] >= self.df_clean['max_start_year']].copy()
+        self.df_clean = self.df_clean.drop('max_start_year', axis=1)
+
+        self.logger.info(f"\n  Rows before: {original_count:,}")
+        self.logger.info(f"  Rows after:  {len(self.df_clean):,}")
+        self.logger.info(f"  Countries:   {self.df_clean['country_id'].nunique()}")
+        self.logger.info(f"  Indicators:  {self.df_clean['indicator_id'].nunique()}")
+        self.logger.info(f"  Pillars:     {self.df_clean['pillar_id'].nunique()}")
+        return self.df_clean
+
+    def verify_no_gaps(self):
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info("STEP 6: VERIFY NO GAPS")
+        self.logger.info("=" * 80)
+
+        expected_countries = len(self.selected_country_ids)
+        verification       = self.df_clean.groupby(['indicator_id', 'year'])['country_id'].nunique().reset_index()
+        verification.columns = ['indicator_id', 'year', 'country_count']
+        all_good = (verification['country_count'] == expected_countries).all()
+
+        if all_good:
+            self.logger.info(f"  VERIFICATION PASSED — all combinations have {expected_countries} countries")
+        else:
+            bad = verification[verification['country_count'] != expected_countries]
+            for _, row in bad.head(10).iterrows():
+                self.logger.error(
+                    f"  Indicator {int(row['indicator_id'])}, Year {int(row['year'])}: "
+                    f"{int(row['country_count'])} countries (expected {expected_countries})"
+                )
+            raise ValueError("Gap verification failed!")
+
+        return True
+
+    def analyze_indicator_availability_by_year(self):
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info("STEP 7: ANALYZE INDICATOR AVAILABILITY BY YEAR")
+        self.logger.info("=" * 80)
+
+        year_stats = self.df_clean.groupby('year').agg({
+            'indicator_id': 'nunique',
+            'country_id'  : 'nunique'
+        }).reset_index()
+        year_stats.columns = ['year', 'indicator_count', 'country_count']
+
+        self.logger.info(f"\n{'Year':<8} {'Indicators':<15} {'Countries':<12} {'Rows'}")
+        self.logger.info("-" * 50)
+        for _, row in year_stats.iterrows():
+            year      = int(row['year'])
+            row_count = len(self.df_clean[self.df_clean['year'] == year])
+            self.logger.info(
+                f"{year:<8} {int(row['indicator_count']):<15} "
+                f"{int(row['country_count']):<12} {row_count:,}"
+            )
+
+        indicator_details = self.df_clean.groupby([
+            'indicator_id', 'indicator_name', 'pillar_name', 'direction'
+        ]).agg({'year': ['min', 'max'], 'country_id': 'nunique'}).reset_index()
+        indicator_details.columns = [
+            'indicator_id', 'indicator_name', 'pillar_name', 'direction',
+            'start_year', 'end_year', 'country_count'
+        ]
+        indicator_details['year_range'] = (
+            indicator_details['start_year'].astype(int).astype(str) + '-' +
+            indicator_details['end_year'].astype(int).astype(str)
+        )
+        indicator_details = indicator_details.sort_values(['pillar_name', 'start_year', 'indicator_name'])
+
+        self.logger.info(f"\nTotal Indicators: {len(indicator_details)}")
+        for pillar, count in indicator_details.groupby('pillar_name').size().items():
+            self.logger.info(f"  {pillar}: {count} indicators")
+
+        self.logger.info(f"\n{'-'*100}")
+        self.logger.info(f"{'ID':<5} {'Indicator Name':<55} {'Pillar':<15} {'Years':<12} {'Dir':<8} {'Countries'}")
+        self.logger.info(f"{'-'*100}")
+        for _, row in indicator_details.iterrows():
+            direction = 'higher+' if row['direction'] == 'higher_better' else 'lower-'
+            self.logger.info(
+                f"{int(row['indicator_id']):<5} {row['indicator_name'][:52]:<55} "
+                f"{row['pillar_name'][:13]:<15} {row['year_range']:<12} "
+                f"{direction:<8} {int(row['country_count'])}"
+            )
+
+        return year_stats
+
+    def save_analytical_table(self):
+        table_name = 'analytical_food_security'
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info(f"STEP 8: SAVE TO [DW/Gold] {table_name} -> fs_asean_gold")
+        self.logger.info("=" * 80)
+
+        try:
+            analytical_df = self.df_clean[[
+                'country_id', 'indicator_id', 'pillar_id', 'time_id', 'value'
+            ]].copy()
+            analytical_df = analytical_df.sort_values(
+                ['time_id', 'country_id', 'indicator_id']
+            ).reset_index(drop=True)
+
+            analytical_df['country_id']   = analytical_df['country_id'].astype(int)
+            analytical_df['indicator_id'] = analytical_df['indicator_id'].astype(int)
+            analytical_df['pillar_id']    = analytical_df['pillar_id'].astype(int)
+            analytical_df['time_id']      = analytical_df['time_id'].astype(int)
+            analytical_df['value']        = analytical_df['value'].astype(float)
+
+            self.logger.info(f"  Total rows: {len(analytical_df):,}")
+
+            schema = [
+                bigquery.SchemaField("country_id",   "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("indicator_id", "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("pillar_id",    "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("time_id",      "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("value",        "FLOAT",   mode="REQUIRED"),
+            ]
+
+            rows_loaded = load_to_bigquery(
+                self.client, analytical_df, table_name,
+                layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema
+            )
+
+            self.pipeline_metadata['rows_loaded'] = rows_loaded
+            log_update(self.client, 'DW', table_name, 'full_load', rows_loaded)
+
+            metadata = {
+                'source_class'       : self.__class__.__name__,
+                'table_name'         : table_name,
+                'execution_timestamp': self.pipeline_start,
+                'duration_seconds'   : (datetime.now() - self.pipeline_start).total_seconds(),
+                'rows_fetched'       : self.pipeline_metadata['rows_fetched'],
+                'rows_transformed'   : rows_loaded,
+                'rows_loaded'        : rows_loaded,
+                'completeness_pct'   : 100.0,
+                'config_snapshot'    : json.dumps({
+                    'start_year'     : self.start_year,
+                    'end_year'       : self.end_year,
+                    'fixed_countries': len(self.selected_country_ids),
+                    'no_gaps'        : True,
+                    'layer'          : 'gold'
+                }),
+                'validation_metrics' : json.dumps({
+                    'fixed_countries' : len(self.selected_country_ids),
+                    'total_indicators': int(self.df_clean['indicator_id'].nunique())
+                })
+            }
+            save_etl_metadata(self.client, metadata)
+
+            self.logger.info(f"  ✓ {table_name}: {rows_loaded:,} rows → [DW/Gold] fs_asean_gold")
+            self.logger.info(f"    Metadata → [AUDIT] etl_metadata")
+            return rows_loaded
+
+        except Exception as e:
+            self.logger.error(f"Error saving: {e}")
+            raise
+
+    def run(self):
+        self.pipeline_start = datetime.now()
+        self.pipeline_metadata['start_time'] = self.pipeline_start
+
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info("Output: analytical_food_security → fs_asean_gold")
+        self.logger.info("=" * 80)
+
+        self.load_source_data()
+        self.determine_year_boundaries()
+        self.filter_complete_indicators_per_country()
+        self.select_countries_with_all_pillars()
+        self.filter_indicators_consistent_across_fixed_countries()
+        self.verify_no_gaps()
+        self.analyze_indicator_availability_by_year()
+        self.save_analytical_table()
+
+        self.pipeline_end = datetime.now()
+        duration          = (self.pipeline_end - self.pipeline_start).total_seconds()
+
+        self.logger.info("\n" + "=" * 80)
+        self.logger.info("COMPLETED")
+        self.logger.info("=" * 80)
+        self.logger.info(f"  Duration   : {duration:.2f}s")
+        self.logger.info(f"  Year Range : {self.start_year}-{self.end_year}")
+        self.logger.info(f"  Countries  : {len(self.selected_country_ids)}")
+        self.logger.info(f"  Indicators : {self.df_clean['indicator_id'].nunique()}")
+        self.logger.info(f"  Rows Loaded: {self.pipeline_metadata['rows_loaded']:,}")
+
+
+# =============================================================================
+# AIRFLOW TASK FUNCTION
+# =============================================================================
+
+def run_analytical_layer():
+    """
+    Airflow task: Build analytical_food_security dari fact_food_security + dims.
+    Dipanggil setelah dimensional_model_to_gold selesai.
+    """
+    from scripts.bigquery_config import get_bigquery_client
+    client = get_bigquery_client()
+    loader = AnalyticalLayerLoader(client)
+    loader.run()
+    print(f"Analytical layer loaded: {loader.pipeline_metadata['rows_loaded']:,} rows")
+
+
+# =============================================================================
+# MAIN EXECUTION
+# =============================================================================
+
+if __name__ == "__main__":
+    print("=" * 80)
+    print("Output: analytical_food_security → fs_asean_gold")
+    print("=" * 80)
+
+    logger = setup_logging()
+    client = get_bigquery_client()
+    loader = AnalyticalLayerLoader(client)
+    loader.run()
+
+    print("\n" + "=" * 80)
+    print("[OK] COMPLETED")
+    print("=" * 80)

scripts/bigquery_cleaned_layer.py

@@ -0,0 +1,581 @@
+"""
+BIGQUERY CLEANED LAYER ETL
+Kimball Data Warehouse Architecture
+
+Kimball ETL Flow yang dijalankan file ini:
+    Input  : STAGING layer (Silver) — staging_integrated  (fs_asean_silver)
+    Output : STAGING layer (Silver) — cleaned_integrated  (fs_asean_silver)
+    Audit  : AUDIT layer            — etl_logs, etl_metadata (fs_asean_audit)
+
+Classes:
+    CleanedDataLoader — Cleaning, enrichment, & load ke Silver layer
+
+Usage:
+    python bigquery_cleaned_layer.py
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import datetime
+import logging
+from typing import Dict
+import json
+
+from scripts.bigquery_config import get_bigquery_client, CONFIG, get_table_id
+from scripts.bigquery_helpers import (
+    log_update,
+    load_to_bigquery,
+    read_from_bigquery,
+    setup_logging,
+    save_etl_metadata,
+)
+from google.cloud import bigquery
+
+
+# =============================================================================
+# LOAD STAGING DATA
+# =============================================================================
+
+def load_staging_data(client: bigquery.Client) -> pd.DataFrame:
+    """Load data dari staging_integrated (STAGING/Silver layer)."""
+    print("\nLoading data from staging_integrated (fs_asean_silver)...")
+    df_staging = read_from_bigquery(client, 'staging_integrated', layer='silver')
+    print(f"  ✓ Loaded        : {len(df_staging):,} rows")
+    print(f"    Columns       : {len(df_staging.columns)}")
+    print(f"    Sources       : {df_staging['source'].nunique()}")
+    print(f"    Indicators    : {df_staging['indicator_standardized'].nunique()}")
+    print(f"    Countries     : {df_staging['country'].nunique()}")
+    print(f"    Year range    : {int(df_staging['year'].min())}-{int(df_staging['year'].max())}")
+    return df_staging
+
+
+# =============================================================================
+# COLUMN CONSTRAINT HELPERS
+# =============================================================================
+
+# Schema constraints — semua varchar max lengths
+COLUMN_CONSTRAINTS = {
+    'source'                : 20,
+    'indicator_original'    : 255,
+    'indicator_standardized': 255,
+    'country'               : 100,
+    'year_range'            : 20,
+    'unit'                  : 20,
+    'pillar'                : 20,
+    'direction'             : 15,   # 'higher_better'=13, 'lower_better'=12
+}
+
+
+def truncate_string(value, max_length: int) -> str:
+    """Truncate string ke max_length, return as-is jika None/NaN."""
+    if pd.isna(value):
+        return value
+    value_str = str(value)
+    return value_str[:max_length] if len(value_str) > max_length else value_str
+
+
+def apply_column_constraints(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Apply column length constraints sesuai schema tabel.
+    Melaporkan kolom mana yang dipotong dan contohnya.
+    """
+    df_constrained    = df.copy()
+    truncation_report = {}
+
+    for column, max_length in COLUMN_CONSTRAINTS.items():
+        if column not in df_constrained.columns:
+            continue
+        mask = (
+            df_constrained[column].notna() &
+            (df_constrained[column].astype(str).str.len() > max_length)
+        )
+        truncated_count = mask.sum()
+        if truncated_count > 0:
+            truncation_report[column] = {
+                'count'     : int(truncated_count),
+                'max_length': max_length,
+                'examples'  : df_constrained[mask][column].head(3).tolist()
+            }
+            df_constrained[column] = df_constrained[column].apply(
+                lambda x: truncate_string(x, max_length)
+            )
+
+    if truncation_report:
+        print("\n  ⚠ Column Truncations Applied:")
+        for column, info in truncation_report.items():
+            print(f"    - {column}: {info['count']} values truncated to {info['max_length']} chars")
+    else:
+        print("\n  ✓ No truncations needed — all values within constraints")
+
+    return df_constrained
+
+
+# =============================================================================
+# COUNTRY NAME STANDARDIZATION
+# =============================================================================
+
+ASEAN_MAPPING = {
+    'BRN'                                : 'Brunei Darussalam',
+    'BRUNEI'                             : 'Brunei Darussalam',
+    'BRUNEI DARUSSALAM'                  : 'Brunei Darussalam',
+    'KHM'                                : 'Cambodia',
+    'CAMBODIA'                           : 'Cambodia',
+    'IDN'                                : 'Indonesia',
+    'INDONESIA'                          : 'Indonesia',
+    'LAO'                                : 'Laos',
+    'LAOS'                               : 'Laos',
+    "LAO PEOPLE'S DEMOCRATIC REPUBLIC"  : 'Laos',
+    'LAO PDR'                            : 'Laos',
+    'MYS'                                : 'Malaysia',
+    'MALAYSIA'                           : 'Malaysia',
+    'MMR'                                : 'Myanmar',
+    'MYANMAR'                            : 'Myanmar',
+    'BURMA'                              : 'Myanmar',
+    'PHL'                                : 'Philippines',
+    'PHILIPPINES'                        : 'Philippines',
+    'SGP'                                : 'Singapore',
+    'SINGAPORE'                          : 'Singapore',
+    'THA'                                : 'Thailand',
+    'THAILAND'                           : 'Thailand',
+    'VNM'                                : 'Vietnam',
+    'VIETNAM'                            : 'Vietnam',
+    'VIET NAM'                           : 'Vietnam',
+}
+
+
+def standardize_country_names_asean(df: pd.DataFrame, country_column: str = 'country') -> tuple:
+    """
+    Standardize country names untuk ASEAN.
+    Ensures country names within varchar(100) constraint.
+
+    Returns:
+        tuple: (df_clean, report_dict)
+    """
+    df_clean = df.copy()
+
+    def map_country(country):
+        if pd.isna(country):
+            return country
+        s = str(country).strip()
+        mapped = ASEAN_MAPPING.get(s.upper(), s)
+        return mapped[:100] if len(mapped) > 100 else mapped
+
+    original               = df_clean[country_column].copy()
+    df_clean[country_column] = df_clean[country_column].apply(map_country)
+    changes = {orig: new for orig, new in zip(original, df_clean[country_column]) if orig != new}
+
+    return df_clean, {
+        'countries_mapped': len(set(changes.keys())),
+        'changes'         : changes,
+    }
+
+
+# =============================================================================
+# PILLAR CLASSIFICATION
+# =============================================================================
+
+def assign_pillar(indicator_name: str) -> str:
+    """
+    Assign pillar berdasarkan keyword indikator.
+    Return values: 'Availability', 'Access', 'Utilization', 'Stability', 'Other'
+    All ≤ 20 chars (varchar(20) constraint).
+    """
+    if pd.isna(indicator_name):
+        return 'Other'
+    ind = str(indicator_name).lower()
+
+    for kw in ['requirement', 'coefficient', 'losses', 'fat supply']:
+        if kw in ind:
+            return 'Other'
+
+    if any(kw in ind for kw in [
+        'adequacy', 'protein supply', 'supply of protein',
+        'dietary energy supply', 'share of dietary energy', 'derived from cereals'
+    ]):
+        return 'Availability'
+
+    if any(kw in ind for kw in [
+        'variability', 'cereal import dependency', 'arable land equipped',
+        'political stability', 'value of food imports in total'
+    ]):
+        return 'Stability'
+
+    if any(kw in ind for kw in [
+        'gdp', 'gross domestic product', 'rail lines', 'road density',
+        'number of moderately', 'number of severely',
+        'number of people undernourished', 'prevalence of moderate',
+        'prevalence of severe', 'prevalence of undernourishment', 'food insecure'
+    ]):
+        return 'Access'
+
+    if any(kw in ind for kw in [
+        'wasting', 'wasted', 'stunted', 'overweight', 'obese', 'obesity',
+        'anemia', 'birthweight', 'breastfeeding', 'drinking water', 'sanitation',
+        'children under 5', 'newborns with low', 'women of reproductive'
+    ]):
+        return 'Utilization'
+
+    return 'Other'
+
+
+# =============================================================================
+# DIRECTION CLASSIFICATION
+# =============================================================================
+
+def assign_direction(indicator_name: str) -> str:
+    """
+    Assign direction berdasarkan indikator.
+    Return values: 'higher_better' (13 chars) atau 'lower_better' (12 chars)
+    Both ≤ 15 chars (varchar(15) constraint).
+    """
+    if pd.isna(indicator_name):
+        return 'higher_better'
+    ind = str(indicator_name).lower()
+
+    # Spesifik lower_better
+    if 'share of dietary energy supply derived from cereals' in ind:
+        return 'lower_better'
+
+    # Higher_better exceptions — cek sebelum lower_better keywords
+    for kw in [
+        'exclusive breastfeeding',
+        'dietary energy supply',
+        'dietary energy supply adequacy',
+        'average fat supply',
+        'average protein supply',
+        'supply of protein of animal origin',
+    ]:
+        if kw in ind:
+            return 'higher_better'
+
+    # Lower_better — masalah yang harus diminimalkan
+    for kw in [
+        'prevalence of undernourishment',
+        'prevalence of severe food insecurity',
+        'prevalence of moderate or severe food insecurity',
+        'prevalence of moderate food insecurity',
+        'prevalence of wasting',
+        'prevalence of stunting',
+        'prevalence of overweight',
+        'prevalence of obesity',
+        'prevalence of anemia',
+        'prevalence of low birthweight',
+        'number of people undernourished',
+        'number of severely food insecure',
+        'number of moderately or severely food insecure',
+        'number of children under 5 years affected by wasting',
+        'number of children under 5 years of age who are overweight',
+        'number of children under 5 years of age who are stunted',
+        'number of newborns with low birthweight',
+        'number of obese adults',
+        'number of women of reproductive age',
+        'percentage of children under 5 years affected by wasting',
+        'percentage of children under 5 years of age who are overweight',
+        'percentage of children under 5 years of age who are stunted',
+        'cereal import dependency',
+        'import dependency',
+        'value of food imports in total merchandise exports',
+        'value of food imports',
+        'variability of food production',
+        'variability of food supply',
+        'per capita food production variability',
+        'per capita food supply variability',
+        'coefficient of variation',
+        'incidence of caloric losses',
+        'food losses',
+    ]:
+        if kw in ind:
+            return 'lower_better'
+
+    return 'higher_better'
+
+
+# =============================================================================
+# CLEANED DATA LOADER
+# =============================================================================
+
+class CleanedDataLoader:
+    """
+    Loader untuk cleaned integrated data ke STAGING layer (Silver).
+
+    Kimball context:
+        Input  : staging_integrated  → STAGING (Silver) — fs_asean_silver
+        Output : cleaned_integrated  → STAGING (Silver) — fs_asean_silver
+        Audit  : etl_logs, etl_metadata → AUDIT — fs_asean_audit
+
+    Pipeline steps:
+        1. Standardize country names (ASEAN)
+        2. Remove missing values
+        3. Remove duplicates
+        4. Add pillar classification
+        5. Add direction classification
+        6. Apply column constraints
+        7. Load ke BigQuery
+        8. Log ke Audit layer
+    """
+
+    SCHEMA = [
+        bigquery.SchemaField("source",                 "STRING",  mode="REQUIRED"),
+        bigquery.SchemaField("indicator_original",     "STRING",  mode="REQUIRED"),
+        bigquery.SchemaField("indicator_standardized", "STRING",  mode="REQUIRED"),
+        bigquery.SchemaField("country",                "STRING",  mode="REQUIRED"),
+        bigquery.SchemaField("year",                   "INTEGER", mode="NULLABLE"),
+        bigquery.SchemaField("year_range",             "STRING",  mode="NULLABLE"),
+        bigquery.SchemaField("value",                  "FLOAT",   mode="NULLABLE"),
+        bigquery.SchemaField("unit",                   "STRING",  mode="NULLABLE"),
+        bigquery.SchemaField("pillar",                 "STRING",  mode="REQUIRED"),
+        bigquery.SchemaField("direction",              "STRING",  mode="REQUIRED"),
+    ]
+
+    def __init__(self, client: bigquery.Client, load_mode: str = 'full_refresh'):
+        self.client       = client
+        self.load_mode    = load_mode
+        self.logger       = logging.getLogger(self.__class__.__name__)
+        self.logger.propagate = False
+        self.table_name   = 'cleaned_integrated'
+        self.target_layer = 'silver'
+
+        self.metadata = {
+            'source_class'      : self.__class__.__name__,
+            'table_name'        : self.table_name,
+            'start_time'        : None,
+            'end_time'          : None,
+            'duration_seconds'  : None,
+            'rows_fetched'      : 0,
+            'rows_transformed'  : 0,
+            'rows_loaded'       : 0,
+            'load_mode'         : load_mode,
+            'validation_metrics': {}
+        }
+
+    # ------------------------------------------------------------------
+    # STEP METHODS
+    # ------------------------------------------------------------------
+
+    def _step_standardize_countries(self, df: pd.DataFrame) -> pd.DataFrame:
+        print("\n  [Step 1/5] Standardize country names...")
+        df, report = standardize_country_names_asean(df, country_column='country')
+        print(f"    ✓ ASEAN countries mapped : {report['countries_mapped']}")
+        unique_countries = sorted(df['country'].unique())
+        print(f"    Countries ({len(unique_countries)}) : {', '.join(unique_countries)}")
+        log_update(self.client, 'STAGING', 'staging_integrated',
+                   'standardize_asean', report['countries_mapped'])
+        return df
+
+    def _step_remove_missing(self, df: pd.DataFrame) -> pd.DataFrame:
+        print("\n  [Step 2/5] Remove missing values...")
+        rows_before = len(df)
+        df_clean    = df.dropna(subset=list(df.columns))
+        rows_after  = len(df_clean)
+        removed     = rows_before - rows_after
+        print(f"    Rows before  : {rows_before:,}")
+        print(f"    Rows after   : {rows_after:,}")
+        print(f"    Rows removed : {removed:,} ({removed/rows_before*100:.1f}%)")
+        print(f"    Retention    : {rows_after/rows_before*100:.1f}%")
+        return df_clean
+
+    def _step_remove_duplicates(self, df: pd.DataFrame) -> pd.DataFrame:
+        print("\n  [Step 3/5] Remove duplicates...")
+        exact_dups = df.duplicated().sum()
+        data_dups  = df.duplicated(subset=['indicator_standardized', 'country', 'year', 'value']).sum()
+        print(f"    Exact duplicates : {exact_dups:,}")
+        print(f"    Data duplicates  : {data_dups:,}")
+        rows_before = len(df)
+        df_clean    = df.drop_duplicates(
+            subset=['indicator_standardized', 'country', 'year'], keep='first'
+        )
+        removed = rows_before - len(df_clean)
+        print(f"    Rows removed     : {removed:,} ({removed/rows_before*100:.1f}%)")
+        return df_clean
+
+    def _step_add_classifications(self, df: pd.DataFrame) -> pd.DataFrame:
+        print("\n  [Step 4/5] Add pillar & direction classification...")
+        df = df.copy()
+        df['pillar']    = df['indicator_standardized'].apply(assign_pillar)
+        df['direction'] = df['indicator_standardized'].apply(assign_direction)
+
+        pillar_counts = df['pillar'].value_counts()
+        print(f"    ✓ Pillar distribution:")
+        for pillar, count in pillar_counts.items():
+            print(f"      - {pillar}: {count:,}")
+
+        direction_counts = df['direction'].value_counts()
+        print(f"    ✓ Direction distribution:")
+        for direction, count in direction_counts.items():
+            pct = count / len(df) * 100
+            print(f"      - {direction}: {count:,} ({pct:.1f}%)")
+        return df
+
+    def _step_apply_constraints(self, df: pd.DataFrame) -> pd.DataFrame:
+        print("\n  [Step 5/5] Apply column constraints...")
+        return apply_column_constraints(df)
+
+    # ------------------------------------------------------------------
+    # VALIDATION
+    # ------------------------------------------------------------------
+
+    def validate_data(self, df: pd.DataFrame) -> Dict:
+        validation = {
+            'total_rows'      : int(len(df)),
+            'total_columns'   : int(len(df.columns)),
+            'duplicate_count' : int(df.duplicated().sum()),
+            'completeness_pct': float(round((1 - df.isnull().sum().sum() / df.size) * 100, 2)),
+            'memory_usage_mb' : float(round(df.memory_usage(deep=True).sum() / 1024**2, 2))
+        }
+        if 'year' in df.columns:
+            validation['year_range'] = {
+                'min'         : int(df['year'].min()) if not df['year'].isnull().all() else None,
+                'max'         : int(df['year'].max()) if not df['year'].isnull().all() else None,
+                'unique_years': int(df['year'].nunique())
+            }
+        for col in ('pillar', 'direction', 'source'):
+            if col in df.columns:
+                validation[f'{col}_breakdown'] = {
+                    str(k): int(v) for k, v in df[col].value_counts().to_dict().items()
+                }
+        if 'indicator_standardized' in df.columns:
+            validation['unique_indicators'] = int(df['indicator_standardized'].nunique())
+        if 'country' in df.columns:
+            validation['unique_countries'] = int(df['country'].nunique())
+
+        # Column length check
+        column_length_check = {}
+        for col, max_len in COLUMN_CONSTRAINTS.items():
+            if col in df.columns:
+                max_actual = df[col].astype(str).str.len().max()
+                column_length_check[col] = {
+                    'max_length_constraint': max_len,
+                    'max_actual_length'    : int(max_actual),
+                    'within_limit'         : bool(max_actual <= max_len)
+                }
+        validation['column_length_check'] = column_length_check
+        return validation
+
+    # ------------------------------------------------------------------
+    # RUN
+    # ------------------------------------------------------------------
+
+    def run(self, df: pd.DataFrame) -> int:
+        """
+        Execute full cleaning pipeline → load ke STAGING (Silver).
+
+        Returns:
+            int: Rows loaded
+        """
+        self.metadata['start_time']   = datetime.now()
+        self.metadata['rows_fetched'] = len(df)
+
+        if df.empty:
+            print("  ERROR: DataFrame is empty, nothing to process.")
+            return 0
+
+        # Pipeline steps
+        df = self._step_standardize_countries(df)
+        df = self._step_remove_missing(df)
+        df = self._step_remove_duplicates(df)
+        df = self._step_add_classifications(df)
+        df = self._step_apply_constraints(df)
+
+        self.metadata['rows_transformed'] = len(df)
+
+        # Validate
+        validation = self.validate_data(df)
+        self.metadata['validation_metrics'] = validation
+
+        all_within_limits = all(
+            info['within_limit']
+            for info in validation.get('column_length_check', {}).values()
+        )
+        if not all_within_limits:
+            print("\n  ⚠ WARNING: Some columns still exceed length constraints!")
+            for col, info in validation['column_length_check'].items():
+                if not info['within_limit']:
+                    print(f"    - {col}: {info['max_actual_length']} > {info['max_length_constraint']}")
+
+        # Load ke Silver
+        print(f"\n  Loading to [STAGING/Silver] {self.table_name} → fs_asean_silver...")
+        rows_loaded = load_to_bigquery(
+            self.client, df, self.table_name,
+            layer='silver',
+            write_disposition="WRITE_TRUNCATE",
+            schema=self.SCHEMA
+        )
+        self.metadata['rows_loaded'] = rows_loaded
+
+        # Audit logs
+        log_update(self.client, 'STAGING', self.table_name, 'full_refresh', rows_loaded)
+
+        # ETL metadata
+        self.metadata['end_time']            = datetime.now()
+        self.metadata['duration_seconds']    = (
+            self.metadata['end_time'] - self.metadata['start_time']
+        ).total_seconds()
+        self.metadata['execution_timestamp'] = self.metadata['start_time']
+        self.metadata['completeness_pct']    = validation.get('completeness_pct', 0)
+        self.metadata['config_snapshot']     = json.dumps({'load_mode': self.load_mode})
+        self.metadata['validation_metrics']  = json.dumps(validation)
+        save_etl_metadata(self.client, self.metadata)
+
+        # Summary
+        print(f"\n  ✓ Cleaned Integration completed: {rows_loaded:,} rows")
+        print(f"    Duration     : {self.metadata['duration_seconds']:.2f}s")
+        print(f"    Completeness : {validation['completeness_pct']:.2f}%")
+        if 'year_range' in validation:
+            yr = validation['year_range']
+            if yr['min'] and yr['max']:
+                print(f"    Year range   : {yr['min']}–{yr['max']}")
+        print(f"    Indicators   : {validation.get('unique_indicators', '-')}")
+        print(f"    Countries    : {validation.get('unique_countries', '-')}")
+        print(f"\n    Schema Validation:")
+        for col, info in validation.get('column_length_check', {}).items():
+            status = "✓" if info['within_limit'] else "✗"
+            print(f"    {status} {col}: {info['max_actual_length']}/{info['max_length_constraint']}")
+        print(f"\n    Metadata → [AUDIT] etl_metadata")
+
+        return rows_loaded
+
+
+# =============================================================================
+# AIRFLOW TASK FUNCTIONS  ← sama polanya dengan raw layer
+# =============================================================================
+
+def run_cleaned_integration():
+    """
+    Airflow task: Load cleaned_integrated dari staging_integrated.
+
+    Dipanggil oleh DAG setelah task staging_integration_to_silver selesai.
+    """
+    from scripts.bigquery_config import get_bigquery_client
+    client     = get_bigquery_client()
+    df_staging = load_staging_data(client)
+    loader     = CleanedDataLoader(client, load_mode='full_refresh')
+    rows       = loader.run(df_staging)
+    print(f"Cleaned layer loaded: {rows:,} rows")
+
+
+# =============================================================================
+# MAIN EXECUTION
+# =============================================================================
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("BIGQUERY CLEANED LAYER ETL")
+    print("Kimball DW Architecture")
+    print("  Input  : STAGING (Silver) → staging_integrated")
+    print("  Output : STAGING (Silver) → cleaned_integrated")
+    print("  Audit  : AUDIT            → etl_logs, etl_metadata")
+    print("=" * 60)
+
+    logger     = setup_logging()
+    client     = get_bigquery_client()
+    df_staging = load_staging_data(client)
+
+    print("\n[1/1] Cleaned Integration → STAGING (Silver)...")
+    loader      = CleanedDataLoader(client, load_mode='full_refresh')
+    final_count = loader.run(df_staging)
+
+    print("\n" + "=" * 60)
+    print("✓ CLEANED LAYER ETL COMPLETED")
+    print(f"  🥈 STAGING (Silver) : cleaned_integrated ({final_count:,} rows)")
+    print(f"  📋 AUDIT            : etl_logs, etl_metadata")
+    print("=" * 60)

scripts/bigquery_config.py

@@ -1,6 +1,6 @@
 """
 BIGQUERY CONFIGURATION FOR FOOD SECURITY DATA INTEGRATION
-Kimball Data Warehouse Architecture
+Kimball Data Warehouse Architecturejson
 
 Dataset Naming:
 - Bronze (fs_asean_bronze) : Raw layer     — data as-is dari sumber
@@ -21,6 +21,7 @@ Kimball ETL Flow:
 """
 
 import os
+import json
 from pathlib import Path
 from google.cloud import bigquery
 from google.oauth2 import service_account
@@ -28,7 +29,7 @@ from google.oauth2 import service_account
 # BIGQUERY CONFIGURATION
 CREDENTIALS_PATH = os.environ.get(
     "GOOGLE_APPLICATION_CREDENTIALS",
-    "/opt/airflow/secrets/food-security-asean-project-826a4d7b302a.json"
+    "/opt/airflow/secrets/food-security-asean-project-3c22d9247bcb.json"
 )
 PROJECT_ID = "food-security-asean-project"
 LOCATION   = "asia-southeast2"
@@ -88,25 +89,6 @@ KIMBALL_LAYER_MAP = {
     "dw"      : "gold",
 }
 
-# SETUP BIGQUERY CLIENT
-
-def get_bigquery_client() -> bigquery.Client:
-    """
-    Create BigQuery client dengan service account credentials
-
-    Returns:
-        bigquery.Client: Authenticated BigQuery client
-    """
-    credentials = service_account.Credentials.from_service_account_file(
-        CREDENTIALS_PATH,
-        scopes=["https://www.googleapis.com/auth/cloud-platform"]
-    )
-    return bigquery.Client(
-        credentials=credentials,
-        project=PROJECT_ID,
-        location=LOCATION
-    )
-
 # MATCHING CONFIGURATION
 
 CONFIG = {
@@ -166,7 +148,6 @@ for directory in [EXPORTS_DIR, LOGS_DIR]:
 # HELPER FUNCTIONS
 
 def get_table_id(table_name: str, layer: str = "bronze") -> str:
-   
     # Resolve Kimball alias ke layer name
     resolved = KIMBALL_LAYER_MAP.get(layer.lower(), layer.lower())
     dataset  = LAYER_DATASET_MAP.get(resolved, DATASET_BRONZE)
@@ -174,17 +155,6 @@ def get_table_id(table_name: str, layer: str = "bronze") -> str:
 
 
 def table_exists(client: bigquery.Client, table_name: str, layer: str = "bronze") -> bool:
-    """
-    Check apakah table ada di BigQuery
-
-    Args:
-        client     : BigQuery client
-        table_name : Nama table
-        layer      : Layer — 'bronze'/'raw', 'silver'/'staging', 'gold'/'dw'
-
-    Returns:
-        bool: True jika table ada
-    """
     try:
         client.get_table(get_table_id(table_name, layer))
         return True
@@ -193,14 +163,6 @@ def table_exists(client: bigquery.Client, table_name: str, layer: str = "bronze"
 
 
 def delete_table(client: bigquery.Client, table_name: str, layer: str = "bronze"):
-    """
-    Delete table jika ada
-
-    Args:
-        client     : BigQuery client
-        table_name : Nama table
-        layer      : Layer — 'bronze'/'raw', 'silver'/'staging', 'gold'/'dw'
-    """
     table_id = get_table_id(table_name, layer)
     try:
         client.delete_table(table_id, not_found_ok=True)
@@ -210,13 +172,6 @@ def delete_table(client: bigquery.Client, table_name: str, layer: str = "bronze"
 
 
 def create_dataset_if_not_exists(client: bigquery.Client, dataset_id: str):
-    """
-    Create dataset jika belum ada
-
-    Args:
-        client     : BigQuery client
-        dataset_id : Dataset ID string
-    """
     full_id = f"{PROJECT_ID}.{dataset_id}"
     try:
         client.get_dataset(full_id)
@@ -229,7 +184,6 @@ def create_dataset_if_not_exists(client: bigquery.Client, dataset_id: str):
 
 
 def create_all_datasets(client: bigquery.Client):
-    """Create semua 3 dataset (Raw/Staging/DW) jika belum ada"""
     print("Setting up BigQuery Datasets (Kimball DW)...")
     for layer, dataset_id in LAYER_DATASET_MAP.items():
         create_dataset_if_not_exists(client, dataset_id)
@@ -238,21 +192,14 @@ def create_all_datasets(client: bigquery.Client):
 # VERIFICATION
 
 def verify_setup() -> bool:
-    """
-    Verify BigQuery setup untuk semua 3 layer (Raw / Staging / DW)
-
-    Checks:
-        1. Credentials file exists
-        2. Koneksi ke BigQuery berhasil
-        3. Semua dataset ada atau berhasil dibuat
-    """
     print("=" * 60)
     print("BIGQUERY SETUP VERIFICATION")
     print("Kimball DW Architecture")
     print("=" * 60)
 
     # 1. Credentials
-    if not os.path.exists(CREDENTIALS_PATH):
+    credentials_json = os.environ.get("GOOGLE_CREDENTIALS_JSON")
+    if not credentials_json and not os.path.exists(CREDENTIALS_PATH):
         print(f"Credentials not found : {CREDENTIALS_PATH}")
         return False
     print(f"✓ Credentials found")
@@ -284,6 +231,7 @@ def verify_setup() -> bool:
     print("=" * 60)
     return True
 
+
 # INITIALIZE ON IMPORT
 
 if __name__ == "__main__":

scripts/bigquery_datasource.py

@@ -11,13 +11,6 @@ Subclass yang menggunakan DataSource:
     FAODataSource       → load ke RAW (Bronze) : raw_fao
     WorldBankDataSource → load ke RAW (Bronze) : raw_worldbank
     UNICEFDataSource    → load ke RAW (Bronze) : raw_unicef
-
-Changes from MySQL version:
-    1. Replace SQLAlchemy engine   → BigQuery client
-    2. Replace to_sql()            → load_table_from_dataframe()
-    3. load_to_database() default layer = 'bronze' (RAW layer)
-    4. log_update() menggunakan label 'RAW' sesuai Kimball terminology
-    5. save_metadata() → save_etl_metadata() ke STAGING layer (Silver)
 """
 
 from abc import ABC, abstractmethod
@@ -27,8 +20,8 @@ from datetime import datetime
 from typing import Dict
 import json
 
-from bigquery_config import get_bigquery_client, get_table_id, table_exists, CONFIG
-from bigquery_helpers import log_update, load_to_bigquery, read_from_bigquery, save_etl_metadata
+from scripts.bigquery_config import get_bigquery_client, get_table_id, table_exists, CONFIG
+from scripts.bigquery_helpers import log_update, load_to_bigquery, read_from_bigquery, save_etl_metadata
 from google.cloud import bigquery
 
 
@@ -42,7 +35,7 @@ class DataSource(ABC):
         transform_data()    → Transform ke format standar
         validate_data()     → Cek kualitas data
         load_to_database()  → Load ke RAW layer (Bronze)
-        save_metadata()     → Simpan metadata ke STAGING layer (Silver)
+        save_metadata()     → Simpan metadata ke AUDIT layer
 
     Subclass wajib implement:
         fetch_data()
@@ -50,22 +43,15 @@ class DataSource(ABC):
     """
 
     def __init__(self, client: bigquery.Client = None):
-        """
-        Initialize DataSource dengan BigQuery client.
-
-        Args:
-            client: BigQuery client (jika None, akan dibuat baru)
-        """
         self.client           = client if client else get_bigquery_client()
         self.logger           = logging.getLogger(self.__class__.__name__)
         self.logger.propagate = False
 
         self.data            = None
         self.table_name      = None
-        self.target_layer    = "bronze"   # RAW layer — default untuk semua data sources
+        self.target_layer    = "bronze"
         self.asean_countries = CONFIG['asean_countries']
 
-        # Metadata untuk tracking reproducibility (disimpan ke STAGING/Silver)
         self.metadata = {
             'source_class'       : self.__class__.__name__,
             'table_name'         : None,
@@ -84,35 +70,13 @@ class DataSource(ABC):
 
     @abstractmethod
     def fetch_data(self) -> pd.DataFrame:
-        """
-        Extract data mentah dari sumber eksternal.
-        WAJIB diimplementasikan oleh subclass.
-        """
         pass
 
     @abstractmethod
     def transform_data(self, df: pd.DataFrame) -> pd.DataFrame:
-        """
-        Transform data ke format standar sebelum load ke RAW layer.
-        WAJIB diimplementasikan oleh subclass.
-        """
         pass
 
     def validate_data(self, df: pd.DataFrame) -> Dict:
-        """
-        Validasi kualitas data hasil transform sebelum load ke RAW layer.
-
-        Metrics yang dihitung:
-            total_rows, total_columns   — dimensi data
-            null_count, null_percentage — kelengkapan per kolom
-            duplicate_count             — duplikasi data
-            completeness_pct            — persentase kelengkapan keseluruhan
-            memory_usage_mb             — ukuran data di memori
-            year_range                  — rentang tahun (jika ada kolom year)
-
-        Returns:
-            Dict: Validation metrics
-        """
         validation = {
             'total_rows'      : int(len(df)),
             'total_columns'   : int(len(df.columns)),
@@ -126,7 +90,6 @@ class DataSource(ABC):
             'memory_usage_mb' : float(round(df.memory_usage(deep=True).sum() / 1024**2, 2))
         }
 
-        # Deteksi kolom year untuk year range info
         year_cols = [col for col in df.columns if 'year' in col.lower() or 'tahun' in col.lower()]
         if year_cols:
             year_col = year_cols[0]
@@ -139,35 +102,18 @@ class DataSource(ABC):
         return validation
 
     def load_to_database(self, df: pd.DataFrame, table_name: str):
-        """
-        Load data ke RAW layer (Bronze) dengan full refresh strategy.
-
-        Kimball context:
-            RAW layer adalah landing zone pertama untuk data mentah dari sumber.
-            Menggunakan WRITE_TRUNCATE (full refresh) karena data sumber
-            bisa berubah setiap kali pipeline dijalankan.
-
-        Args:
-            df         : DataFrame hasil transform
-            table_name : Nama table tujuan di RAW layer (Bronze)
-
-        Audit:
-            Setiap load dicatat ke etl_logs di STAGING layer (Silver)
-        """
         try:
-            # Load ke RAW layer (Bronze) — full refresh
             load_to_bigquery(
                 self.client,
                 df,
                 table_name,
-                layer='bronze',                   # RAW layer
-                write_disposition="WRITE_TRUNCATE" # Full refresh
+                layer='bronze',
+                write_disposition="WRITE_TRUNCATE"
             )
 
-            # Audit log ke STAGING layer (Silver)
             log_update(
                 self.client,
-                layer='RAW',                      # Label Kimball
+                layer='RAW',
                 table_name=table_name,
                 update_method='full_refresh',
                 rows_affected=len(df)
@@ -186,49 +132,20 @@ class DataSource(ABC):
             raise
 
     def save_metadata(self):
-        """
-        Simpan metadata eksekusi ETL ke STAGING layer (Silver).
-
-        Kimball context:
-            ETL metadata (execution time, row counts, completeness, dll.)
-            disimpan di Staging layer sebagai operational/audit table,
-            bukan bagian dari Star Schema di DW layer.
-
-        Metadata yang disimpan:
-            source_class, table_name, execution_timestamp,
-            duration_seconds, rows_fetched/transformed/loaded,
-            completeness_pct, config_snapshot, validation_metrics
-        """
         try:
             self.metadata['table_name'] = self.table_name
 
-            # Pastikan validation_metrics dalam format JSON string
             if isinstance(self.metadata.get('validation_metrics'), dict):
                 self.metadata['validation_metrics'] = json.dumps(
                     self.metadata['validation_metrics']
                 )
 
-            # Save ke STAGING layer (Silver) via helper
             save_etl_metadata(self.client, self.metadata)
 
         except Exception as e:
-            # Silent fail — metadata tracking tidak boleh menghentikan proses ETL
-            self.logger.warning(f"Failed to save ETL metadata to STAGING: {str(e)}")
+            self.logger.warning(f"Failed to save ETL metadata to AUDIT: {str(e)}")
 
     def run(self) -> pd.DataFrame:
-        """
-        Jalankan full ETL pipeline: Extract → Transform → Validate → Load → Metadata.
-
-        Kimball ETL steps:
-            1. EXTRACT   — fetch_data()        : Ambil dari sumber eksternal
-            2. TRANSFORM — transform_data()    : Standardize format
-            3. VALIDATE  — validate_data()     : Cek kualitas
-            4. LOAD      — load_to_database()  : Load ke RAW layer (Bronze)
-            5. METADATA  — save_metadata()     : Simpan ke STAGING layer (Silver)
-
-        Returns:
-            pd.DataFrame: Data yang sudah di-load ke RAW layer
-        """
         start_time = datetime.now()
         self.metadata['execution_timestamp'] = start_time
 
@@ -254,7 +171,7 @@ class DataSource(ABC):
             self.load_to_database(self.data, self.table_name)
             self.metadata['rows_loaded'] = len(self.data)
 
-            # 5. METADATA → STAGING layer (Silver)
+            # 5. METADATA → AUDIT layer
             end_time = datetime.now()
             self.metadata['duration_seconds'] = (end_time - start_time).total_seconds()
             self.save_metadata()
@@ -267,5 +184,5 @@ class DataSource(ABC):
 
 print("DataSource base class loaded — Kimball DW Architecture")
 print("  Default target layer : RAW (Bronze)")
-print("  Audit logs           : STAGING (Silver) via etl_logs")
-print("  ETL metadata         : STAGING (Silver) via etl_metadata")
+print("  Audit logs           : AUDIT via etl_logs")
+print("  ETL metadata         : AUDIT via etl_metadata")

scripts/bigquery_dimensional_model.py

@@ -0,0 +1,850 @@
+"""
+BIGQUERY DIMENSIONAL MODEL LOAD
+Kimball Data Warehouse Architecture
+
+Kimball ETL Flow yang dijalankan file ini:
+    Input  : STAGING layer (Silver) — cleaned_integrated  (fs_asean_silver)
+    Output : DW      layer (Gold)   — dim_*, fact_*        (fs_asean_gold)
+    Audit  : AUDIT   layer          — etl_logs, etl_metadata (fs_asean_audit)
+
+Classes:
+    DimensionalModelLoader — Build Star Schema & load ke Gold layer
+
+Usage:
+    python bigquery_dimensional_model.py
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import datetime
+import logging
+from typing import Dict, List
+import json
+import sys
+
+from scripts.bigquery_config import get_bigquery_client, CONFIG, get_table_id
+from scripts.bigquery_helpers import (
+    log_update,
+    load_to_bigquery,
+    read_from_bigquery,
+    setup_logging,
+    truncate_table,
+    save_etl_metadata,
+)
+from google.cloud import bigquery
+
+if hasattr(sys.stdout, 'reconfigure'):
+    sys.stdout.reconfigure(encoding='utf-8')
+
+
+# =============================================================================
+# DIMENSIONAL MODEL LOADER
+# =============================================================================
+
+class DimensionalModelLoader:
+    """
+    Loader untuk dimensional model ke DW layer (Gold) — fs_asean_gold.
+
+    Kimball context:
+        Input  : cleaned_integrated → STAGING (Silver) — fs_asean_silver
+        Output : dim_* + fact_*     → DW     (Gold)   — fs_asean_gold
+        Audit  : etl_logs, etl_metadata → AUDIT — fs_asean_audit
+
+    Pipeline steps:
+        1. Load dim_country
+        2. Load dim_indicator
+        3. Load dim_time
+        4. Load dim_source
+        5. Load dim_pillar
+        6. Load fact_food_security (resolve FK dari Gold dims)
+        7. Validate constraints & data load
+    """
+
+    def __init__(self, client: bigquery.Client, df_clean: pd.DataFrame):
+        self.client       = client
+        self.df_clean     = df_clean
+        self.logger       = logging.getLogger(self.__class__.__name__)
+        self.logger.propagate = False
+        self.target_layer = 'gold'
+
+        self.load_metadata = {
+            'dim_country'       : {'start_time': None, 'end_time': None, 'rows_loaded': 0, 'status': 'pending'},
+            'dim_indicator'     : {'start_time': None, 'end_time': None, 'rows_loaded': 0, 'status': 'pending'},
+            'dim_time'          : {'start_time': None, 'end_time': None, 'rows_loaded': 0, 'status': 'pending'},
+            'dim_source'        : {'start_time': None, 'end_time': None, 'rows_loaded': 0, 'status': 'pending'},
+            'dim_pillar'        : {'start_time': None, 'end_time': None, 'rows_loaded': 0, 'status': 'pending'},
+            'fact_food_security': {'start_time': None, 'end_time': None, 'rows_loaded': 0, 'status': 'pending'},
+        }
+
+        self.pipeline_metadata = {
+            'source_class'      : self.__class__.__name__,
+            'start_time'        : None,
+            'end_time'          : None,
+            'duration_seconds'  : None,
+            'rows_fetched'      : 0,
+            'rows_transformed'  : 0,
+            'rows_loaded'       : 0,
+            'validation_metrics': {}
+        }
+
+    # ------------------------------------------------------------------
+    # CONSTRAINT HELPERS
+    # ------------------------------------------------------------------
+
+    def _add_primary_key(self, table_name: str, column_name: str):
+        table_id = get_table_id(table_name, layer='gold')
+        query    = f"ALTER TABLE `{table_id}` ADD PRIMARY KEY ({column_name}) NOT ENFORCED"
+        try:
+            self.client.query(query).result()
+            self.logger.info(f"    [OK] PRIMARY KEY: {table_name}({column_name})")
+        except Exception as e:
+            if "already exists" in str(e).lower():
+                self.logger.info(f"    [INFO] PRIMARY KEY already exists: {table_name}({column_name})")
+            else:
+                self.logger.warning(f"    [WARN] Could not add PRIMARY KEY to {table_name}.{column_name}: {e}")
+
+    def _add_foreign_key(self, table_name: str, fk_column: str,
+                         ref_table: str, ref_column: str):
+        table_id        = get_table_id(table_name, layer='gold')
+        ref_table_id    = get_table_id(ref_table,  layer='gold')
+        constraint_name = f"fk_{table_name}_{fk_column}"
+        query = f"""
+        ALTER TABLE `{table_id}`
+        ADD CONSTRAINT {constraint_name}
+        FOREIGN KEY ({fk_column})
+        REFERENCES `{ref_table_id}`({ref_column})
+        NOT ENFORCED
+        """
+        try:
+            self.client.query(query).result()
+            self.logger.info(f"    [OK] FK: {table_name}.{fk_column} → {ref_table}.{ref_column}")
+        except Exception as e:
+            if "already exists" in str(e).lower():
+                self.logger.info(f"    [INFO] FK already exists: {constraint_name}")
+            else:
+                self.logger.warning(f"    [WARN] Could not add FK {constraint_name}: {e}")
+
+    # ------------------------------------------------------------------
+    # METADATA HELPER
+    # ------------------------------------------------------------------
+
+    def _save_table_metadata(self, table_name: str):
+        meta = self.load_metadata[table_name]
+        metadata = {
+            'source_class'       : self.__class__.__name__,
+            'table_name'         : table_name,
+            'execution_timestamp': meta['start_time'],
+            'duration_seconds'   : (meta['end_time'] - meta['start_time']).total_seconds()
+                                    if meta['end_time'] else 0,
+            'rows_fetched'       : 0,
+            'rows_transformed'   : meta['rows_loaded'],
+            'rows_loaded'        : meta['rows_loaded'],
+            'completeness_pct'   : 100.0 if meta['status'] == 'success' else 0.0,
+            'config_snapshot'    : json.dumps({'load_mode': 'full_refresh', 'layer': 'gold'}),
+            'validation_metrics' : json.dumps({'status': meta['status'], 'rows': meta['rows_loaded']})
+        }
+        try:
+            save_etl_metadata(self.client, metadata)
+            self.logger.info(f"    Metadata → [AUDIT] etl_metadata")
+        except Exception as e:
+            self.logger.warning(f"    [WARN] Could not save metadata for {table_name}: {e}")
+
+    # ------------------------------------------------------------------
+    # DIMENSION LOADERS
+    # ------------------------------------------------------------------
+
+    def load_dim_time(self):
+        table_name = 'dim_time'
+        self.load_metadata[table_name]['start_time'] = datetime.now()
+        self.logger.info("Loading dim_time → [DW/Gold] fs_asean_gold...")
+
+        try:
+            if 'year_range' in self.df_clean.columns:
+                dim_time = self.df_clean[['year', 'year_range']].drop_duplicates().copy()
+            else:
+                dim_time              = self.df_clean[['year']].drop_duplicates().copy()
+                dim_time['year_range'] = None
+
+            dim_time['year'] = dim_time['year'].astype(int)
+
+            def parse_year_range(row):
+                year       = row['year']
+                year_range = row.get('year_range')
+                start_year = year
+                end_year   = year
+                if pd.notna(year_range) and year_range is not None:
+                    yr_str = str(year_range).strip()
+                    if yr_str and yr_str != 'nan':
+                        if '-' in yr_str:
+                            parts = yr_str.split('-')
+                            if len(parts) == 2:
+                                try:
+                                    start_year = int(parts[0].strip())
+                                    end_year   = int(parts[1].strip())
+                                    year       = (start_year + end_year) // 2
+                                except Exception:
+                                    pass
+                        else:
+                            try:
+                                single     = int(yr_str)
+                                start_year = single
+                                end_year   = single
+                                year       = single
+                            except Exception:
+                                pass
+                return pd.Series({'year': year, 'start_year': start_year, 'end_year': end_year})
+
+            parsed                   = dim_time.apply(parse_year_range, axis=1)
+            dim_time['year']         = parsed['year'].astype(int)
+            dim_time['start_year']   = parsed['start_year'].astype(int)
+            dim_time['end_year']     = parsed['end_year'].astype(int)
+            dim_time['is_year_range'] = (dim_time['start_year'] != dim_time['end_year'])
+            dim_time['decade']        = (dim_time['year'] // 10) * 10
+            dim_time['is_range']      = (dim_time['start_year'] != dim_time['end_year']).astype(int)
+            dim_time = dim_time.sort_values(['is_range', 'start_year'], ascending=[True, True])
+            dim_time = dim_time.drop(['is_range', 'year_range'], axis=1, errors='ignore')
+            dim_time = dim_time.drop_duplicates(subset=['start_year', 'end_year'], keep='first')
+
+            dim_time_final = dim_time[['year', 'start_year', 'end_year', 'decade', 'is_year_range']].copy()
+            dim_time_final = dim_time_final.reset_index(drop=True)
+            dim_time_final.insert(0, 'time_id', range(1, len(dim_time_final) + 1))
+
+            schema = [
+                bigquery.SchemaField("time_id",       "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("year",          "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("start_year",    "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("end_year",      "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("decade",        "INTEGER", mode="NULLABLE"),
+                bigquery.SchemaField("is_year_range", "BOOLEAN", mode="NULLABLE"),
+            ]
+
+            rows_loaded = load_to_bigquery(
+                self.client, dim_time_final, table_name,
+                layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema
+            )
+            self._add_primary_key(table_name, 'time_id')
+
+            self.load_metadata[table_name].update(
+                {'rows_loaded': rows_loaded, 'status': 'success', 'end_time': datetime.now()}
+            )
+            log_update(self.client, 'DW', table_name, 'full_load', rows_loaded)
+            self._save_table_metadata(table_name)
+            self.logger.info(f"  ✓ dim_time: {rows_loaded} rows\n")
+            return rows_loaded
+
+        except Exception as e:
+            self.load_metadata[table_name].update({'status': 'failed', 'end_time': datetime.now()})
+            log_update(self.client, 'DW', table_name, 'full_load', 0, 'failed', str(e))
+            raise
+
+    def load_dim_country(self):
+        table_name = 'dim_country'
+        self.load_metadata[table_name]['start_time'] = datetime.now()
+        self.logger.info("Loading dim_country → [DW/Gold] fs_asean_gold...")
+
+        try:
+            dim_country            = self.df_clean[['country']].drop_duplicates().copy()
+            dim_country.columns    = ['country_name']
+
+            region_mapping = {
+                'Brunei Darussalam': ('Southeast Asia', 'ASEAN'),
+                'Cambodia'         : ('Southeast Asia', 'ASEAN'),
+                'Indonesia'        : ('Southeast Asia', 'ASEAN'),
+                'Laos'             : ('Southeast Asia', 'ASEAN'),
+                'Malaysia'         : ('Southeast Asia', 'ASEAN'),
+                'Myanmar'          : ('Southeast Asia', 'ASEAN'),
+                'Philippines'      : ('Southeast Asia', 'ASEAN'),
+                'Singapore'        : ('Southeast Asia', 'ASEAN'),
+                'Thailand'         : ('Southeast Asia', 'ASEAN'),
+                'Vietnam'          : ('Southeast Asia', 'ASEAN'),
+            }
+            iso_mapping = {
+                'Brunei Darussalam': 'BRN', 'Cambodia': 'KHM', 'Indonesia': 'IDN',
+                'Laos': 'LAO', 'Malaysia': 'MYS', 'Myanmar': 'MMR',
+                'Philippines': 'PHL', 'Singapore': 'SGP', 'Thailand': 'THA', 'Vietnam': 'VNM',
+            }
+
+            dim_country['region']    = dim_country['country_name'].map(
+                lambda x: region_mapping.get(x, ('Unknown', 'Unknown'))[0])
+            dim_country['subregion'] = dim_country['country_name'].map(
+                lambda x: region_mapping.get(x, ('Unknown', 'Unknown'))[1])
+            dim_country['iso_code']  = dim_country['country_name'].map(iso_mapping)
+
+            dim_country_final = dim_country[['country_name', 'region', 'subregion', 'iso_code']].copy()
+            dim_country_final = dim_country_final.reset_index(drop=True)
+            dim_country_final.insert(0, 'country_id', range(1, len(dim_country_final) + 1))
+
+            schema = [
+                bigquery.SchemaField("country_id",   "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("country_name", "STRING",  mode="REQUIRED"),
+                bigquery.SchemaField("region",       "STRING",  mode="NULLABLE"),
+                bigquery.SchemaField("subregion",    "STRING",  mode="NULLABLE"),
+                bigquery.SchemaField("iso_code",     "STRING",  mode="NULLABLE"),
+            ]
+
+            rows_loaded = load_to_bigquery(
+                self.client, dim_country_final, table_name,
+                layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema
+            )
+            self._add_primary_key(table_name, 'country_id')
+
+            self.load_metadata[table_name].update(
+                {'rows_loaded': rows_loaded, 'status': 'success', 'end_time': datetime.now()}
+            )
+            log_update(self.client, 'DW', table_name, 'full_load', rows_loaded)
+            self._save_table_metadata(table_name)
+            self.logger.info(f"  ✓ dim_country: {rows_loaded} rows\n")
+            return rows_loaded
+
+        except Exception as e:
+            self.load_metadata[table_name].update({'status': 'failed', 'end_time': datetime.now()})
+            log_update(self.client, 'DW', table_name, 'full_load', 0, 'failed', str(e))
+            raise
+
+    def load_dim_indicator(self):
+        table_name = 'dim_indicator'
+        self.load_metadata[table_name]['start_time'] = datetime.now()
+        self.logger.info("Loading dim_indicator → [DW/Gold] fs_asean_gold...")
+
+        try:
+            has_direction = 'direction'          in self.df_clean.columns
+            has_unit      = 'unit'               in self.df_clean.columns
+            has_category  = 'indicator_category' in self.df_clean.columns
+
+            dim_indicator          = self.df_clean[['indicator_standardized']].drop_duplicates().copy()
+            dim_indicator.columns  = ['indicator_name']
+
+            if has_unit:
+                unit_map = self.df_clean[['indicator_standardized', 'unit']].drop_duplicates()
+                unit_map.columns  = ['indicator_name', 'unit']
+                dim_indicator     = dim_indicator.merge(unit_map, on='indicator_name', how='left')
+            else:
+                dim_indicator['unit'] = None
+
+            if has_direction:
+                dir_map = self.df_clean[['indicator_standardized', 'direction']].drop_duplicates()
+                dir_map.columns   = ['indicator_name', 'direction']
+                dim_indicator     = dim_indicator.merge(dir_map, on='indicator_name', how='left')
+                self.logger.info("  [OK] direction column from cleaned_integrated")
+            else:
+                dim_indicator['direction'] = 'higher_better'
+                self.logger.warning("  [WARN] direction not found, default: higher_better")
+
+            if has_category:
+                cat_map = self.df_clean[['indicator_standardized', 'indicator_category']].drop_duplicates()
+                cat_map.columns   = ['indicator_name', 'indicator_category']
+                dim_indicator     = dim_indicator.merge(cat_map, on='indicator_name', how='left')
+            else:
+                def categorize_indicator(name):
+                    n = str(name).lower()
+                    if any(w in n for w in ['undernourishment', 'malnutrition', 'stunting',
+                                             'wasting', 'anemia', 'food security', 'food insecure', 'hunger']):
+                        return 'Health & Nutrition'
+                    elif any(w in n for w in ['production', 'yield', 'cereal', 'crop',
+                                               'import dependency', 'share of dietary']):
+                        return 'Agricultural Production'
+                    elif any(w in n for w in ['import', 'export', 'trade']):
+                        return 'Trade'
+                    elif any(w in n for w in ['gdp', 'income', 'economic']):
+                        return 'Economic'
+                    elif any(w in n for w in ['water', 'sanitation', 'infrastructure', 'rail']):
+                        return 'Infrastructure'
+                    else:
+                        return 'Other'
+                dim_indicator['indicator_category'] = dim_indicator['indicator_name'].apply(categorize_indicator)
+
+            dim_indicator     = dim_indicator.drop_duplicates(subset=['indicator_name'], keep='first')
+            dim_indicator_final = dim_indicator[
+                ['indicator_name', 'indicator_category', 'unit', 'direction']
+            ].copy()
+            dim_indicator_final = dim_indicator_final.reset_index(drop=True)
+            dim_indicator_final.insert(0, 'indicator_id', range(1, len(dim_indicator_final) + 1))
+
+            schema = [
+                bigquery.SchemaField("indicator_id",       "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("indicator_name",     "STRING",  mode="REQUIRED"),
+                bigquery.SchemaField("indicator_category", "STRING",  mode="REQUIRED"),
+                bigquery.SchemaField("unit",               "STRING",  mode="NULLABLE"),
+                bigquery.SchemaField("direction",          "STRING",  mode="REQUIRED"),
+            ]
+
+            rows_loaded = load_to_bigquery(
+                self.client, dim_indicator_final, table_name,
+                layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema
+            )
+            self._add_primary_key(table_name, 'indicator_id')
+
+            for label, col in [('Categories', 'indicator_category'), ('Direction', 'direction')]:
+                self.logger.info(f"  {label}:")
+                for val, cnt in dim_indicator_final[col].value_counts().items():
+                    self.logger.info(f"    - {val}: {cnt} ({cnt/len(dim_indicator_final)*100:.1f}%)")
+
+            self.load_metadata[table_name].update(
+                {'rows_loaded': rows_loaded, 'status': 'success', 'end_time': datetime.now()}
+            )
+            log_update(self.client, 'DW', table_name, 'full_load', rows_loaded)
+            self._save_table_metadata(table_name)
+            self.logger.info(f"  ✓ dim_indicator: {rows_loaded} rows\n")
+            return rows_loaded
+
+        except Exception as e:
+            self.load_metadata[table_name].update({'status': 'failed', 'end_time': datetime.now()})
+            log_update(self.client, 'DW', table_name, 'full_load', 0, 'failed', str(e))
+            raise
+
+    def load_dim_source(self):
+        table_name = 'dim_source'
+        self.load_metadata[table_name]['start_time'] = datetime.now()
+        self.logger.info("Loading dim_source → [DW/Gold] fs_asean_gold...")
+
+        try:
+            source_details = {
+                'FAO': {
+                    'source_type'  : 'International Organization',
+                    'organization' : 'Food and Agriculture Organization',
+                    'access_method': 'Python Library (faostat)',
+                    'api_endpoint' : None,
+                },
+                'World Bank': {
+                    'source_type'  : 'International Organization',
+                    'organization' : 'The World Bank',
+                    'access_method': 'Python Library (wbgapi)',
+                    'api_endpoint' : None,
+                },
+                'UNICEF': {
+                    'source_type'  : 'International Organization',
+                    'organization' : "United Nations Children's Fund",
+                    'access_method': 'SDMX API',
+                    'api_endpoint' : 'https://sdmx.data.unicef.org/ws/public/sdmxapi/rest',
+                },
+            }
+
+            sources_data = []
+            for source in self.df_clean['source'].unique():
+                detail = source_details.get(source, {
+                    'source_type'  : 'International Organization',
+                    'organization' : source,
+                    'access_method': 'Unknown',
+                    'api_endpoint' : None,
+                })
+                sources_data.append({'source_name': source, **detail})
+
+            dim_source_final = pd.DataFrame(sources_data)[
+                ['source_name', 'source_type', 'organization', 'access_method', 'api_endpoint']
+            ].copy()
+            dim_source_final = dim_source_final.reset_index(drop=True)
+            dim_source_final.insert(0, 'source_id', range(1, len(dim_source_final) + 1))
+
+            schema = [
+                bigquery.SchemaField("source_id",     "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("source_name",   "STRING",  mode="REQUIRED"),
+                bigquery.SchemaField("source_type",   "STRING",  mode="NULLABLE"),
+                bigquery.SchemaField("organization",  "STRING",  mode="NULLABLE"),
+                bigquery.SchemaField("access_method", "STRING",  mode="NULLABLE"),
+                bigquery.SchemaField("api_endpoint",  "STRING",  mode="NULLABLE"),
+            ]
+
+            rows_loaded = load_to_bigquery(
+                self.client, dim_source_final, table_name,
+                layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema
+            )
+            self._add_primary_key(table_name, 'source_id')
+
+            self.load_metadata[table_name].update(
+                {'rows_loaded': rows_loaded, 'status': 'success', 'end_time': datetime.now()}
+            )
+            log_update(self.client, 'DW', table_name, 'full_load', rows_loaded)
+            self._save_table_metadata(table_name)
+            self.logger.info(f"  ✓ dim_source: {rows_loaded} rows\n")
+            return rows_loaded
+
+        except Exception as e:
+            self.load_metadata[table_name].update({'status': 'failed', 'end_time': datetime.now()})
+            log_update(self.client, 'DW', table_name, 'full_load', 0, 'failed', str(e))
+            raise
+
+    def load_dim_pillar(self):
+        table_name = 'dim_pillar'
+        self.load_metadata[table_name]['start_time'] = datetime.now()
+        self.logger.info("Loading dim_pillar → [DW/Gold] fs_asean_gold...")
+
+        try:
+            pillar_codes = {
+                'Availability': 'AVL', 'Access'  : 'ACC',
+                'Utilization' : 'UTL', 'Stability': 'STB', 'Other': 'OTH',
+            }
+            pillars_data = [
+                {'pillar_name': p, 'pillar_code': pillar_codes.get(p, 'OTH')}
+                for p in self.df_clean['pillar'].unique()
+            ]
+
+            dim_pillar_final = pd.DataFrame(pillars_data).sort_values('pillar_name')[
+                ['pillar_name', 'pillar_code']
+            ].copy()
+            dim_pillar_final = dim_pillar_final.reset_index(drop=True)
+            dim_pillar_final.insert(0, 'pillar_id', range(1, len(dim_pillar_final) + 1))
+
+            schema = [
+                bigquery.SchemaField("pillar_id",   "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("pillar_name", "STRING",  mode="REQUIRED"),
+                bigquery.SchemaField("pillar_code", "STRING",  mode="NULLABLE"),
+            ]
+
+            rows_loaded = load_to_bigquery(
+                self.client, dim_pillar_final, table_name,
+                layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema
+            )
+            self._add_primary_key(table_name, 'pillar_id')
+
+            self.load_metadata[table_name].update(
+                {'rows_loaded': rows_loaded, 'status': 'success', 'end_time': datetime.now()}
+            )
+            log_update(self.client, 'DW', table_name, 'full_load', rows_loaded)
+            self._save_table_metadata(table_name)
+            self.logger.info(f"  ✓ dim_pillar: {rows_loaded} rows\n")
+            return rows_loaded
+
+        except Exception as e:
+            self.load_metadata[table_name].update({'status': 'failed', 'end_time': datetime.now()})
+            log_update(self.client, 'DW', table_name, 'full_load', 0, 'failed', str(e))
+            raise
+
+    # ------------------------------------------------------------------
+    # FACT LOADER
+    # ------------------------------------------------------------------
+
+    def load_fact_food_security(self):
+        table_name = 'fact_food_security'
+        self.load_metadata[table_name]['start_time'] = datetime.now()
+        self.logger.info("Loading fact_food_security → [DW/Gold] fs_asean_gold...")
+
+        try:
+            # Load dims dari Gold untuk FK resolution
+            dim_country   = read_from_bigquery(self.client, 'dim_country',   layer='gold')
+            dim_indicator = read_from_bigquery(self.client, 'dim_indicator', layer='gold')
+            dim_time      = read_from_bigquery(self.client, 'dim_time',      layer='gold')
+            dim_source    = read_from_bigquery(self.client, 'dim_source',    layer='gold')
+            dim_pillar    = read_from_bigquery(self.client, 'dim_pillar',    layer='gold')
+
+            fact_table = self.df_clean.copy()
+
+            def parse_year_range_for_merge(row):
+                year       = row['year']
+                year_range = row.get('year_range')
+                start_year = year
+                end_year   = year
+                if pd.notna(year_range) and year_range is not None:
+                    yr_str = str(year_range).strip()
+                    if yr_str and yr_str != 'nan':
+                        if '-' in yr_str:
+                            parts = yr_str.split('-')
+                            if len(parts) == 2:
+                                try:
+                                    start_year = int(parts[0].strip())
+                                    end_year   = int(parts[1].strip())
+                                except Exception:
+                                    pass
+                        else:
+                            try:
+                                single     = int(yr_str)
+                                start_year = single
+                                end_year   = single
+                            except Exception:
+                                pass
+                return pd.Series({'start_year': start_year, 'end_year': end_year})
+
+            if 'year_range' in fact_table.columns:
+                parsed                   = fact_table.apply(parse_year_range_for_merge, axis=1)
+                fact_table['start_year'] = parsed['start_year'].astype(int)
+                fact_table['end_year']   = parsed['end_year'].astype(int)
+            else:
+                fact_table['start_year'] = fact_table['year'].astype(int)
+                fact_table['end_year']   = fact_table['year'].astype(int)
+
+            # Resolve FKs
+            fact_table = fact_table.merge(
+                dim_country[['country_id', 'country_name']].rename(columns={'country_name': 'country'}),
+                on='country', how='left'
+            )
+            fact_table = fact_table.merge(
+                dim_indicator[['indicator_id', 'indicator_name']].rename(
+                    columns={'indicator_name': 'indicator_standardized'}),
+                on='indicator_standardized', how='left'
+            )
+            fact_table = fact_table.merge(
+                dim_time[['time_id', 'start_year', 'end_year']],
+                on=['start_year', 'end_year'], how='left'
+            )
+            fact_table = fact_table.merge(
+                dim_source[['source_id', 'source_name']].rename(columns={'source_name': 'source'}),
+                on='source', how='left'
+            )
+            fact_table = fact_table.merge(
+                dim_pillar[['pillar_id', 'pillar_name']].rename(columns={'pillar_name': 'pillar'}),
+                on='pillar', how='left'
+            )
+
+            # Filter hanya row dengan FK lengkap
+            fact_table = fact_table[
+                fact_table['country_id'].notna()   &
+                fact_table['indicator_id'].notna() &
+                fact_table['time_id'].notna()      &
+                fact_table['source_id'].notna()    &
+                fact_table['pillar_id'].notna()
+            ]
+
+            fact_final = fact_table[
+                ['country_id', 'indicator_id', 'time_id', 'source_id', 'pillar_id', 'value']
+            ].copy()
+            fact_final['data_quality_score'] = 0.95
+
+            for col in ['country_id', 'indicator_id', 'time_id', 'source_id', 'pillar_id']:
+                fact_final[col] = fact_final[col].astype(int)
+            fact_final['value'] = fact_final['value'].astype(float)
+
+            fact_final = fact_final.reset_index(drop=True)
+            fact_final.insert(0, 'fact_id', range(1, len(fact_final) + 1))
+
+            schema = [
+                bigquery.SchemaField("fact_id",            "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("country_id",         "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("indicator_id",       "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("time_id",            "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("source_id",          "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("pillar_id",          "INTEGER", mode="REQUIRED"),
+                bigquery.SchemaField("value",              "FLOAT",   mode="NULLABLE"),
+                bigquery.SchemaField("data_quality_score", "FLOAT",   mode="NULLABLE"),
+            ]
+
+            rows_loaded = load_to_bigquery(
+                self.client, fact_final, table_name,
+                layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema
+            )
+
+            # Add PK + FKs
+            self._add_primary_key(table_name, 'fact_id')
+            self._add_foreign_key(table_name, 'country_id',   'dim_country',   'country_id')
+            self._add_foreign_key(table_name, 'indicator_id', 'dim_indicator', 'indicator_id')
+            self._add_foreign_key(table_name, 'time_id',      'dim_time',      'time_id')
+            self._add_foreign_key(table_name, 'source_id',    'dim_source',    'source_id')
+            self._add_foreign_key(table_name, 'pillar_id',    'dim_pillar',    'pillar_id')
+
+            self.load_metadata[table_name].update(
+                {'rows_loaded': rows_loaded, 'status': 'success', 'end_time': datetime.now()}
+            )
+            log_update(self.client, 'DW', table_name, 'full_load', rows_loaded)
+            self._save_table_metadata(table_name)
+            self.logger.info(f"  ✓ fact_food_security: {rows_loaded:,} rows\n")
+            return rows_loaded
+
+        except Exception as e:
+            self.load_metadata[table_name].update({'status': 'failed', 'end_time': datetime.now()})
+            log_update(self.client, 'DW', table_name, 'full_load', 0, 'failed', str(e))
+            raise
+
+    # ------------------------------------------------------------------
+    # VALIDATION
+    # ------------------------------------------------------------------
+
+    def validate_constraints(self):
+        self.logger.info("\n" + "=" * 60)
+        self.logger.info("CONSTRAINT VALIDATION — fs_asean_gold")
+        self.logger.info("=" * 60)
+        try:
+            gold_dataset = CONFIG['bigquery']['dataset_gold']
+            query = f"""
+            SELECT table_name, constraint_name, constraint_type
+            FROM `{CONFIG['bigquery']['project_id']}.{gold_dataset}.INFORMATION_SCHEMA.TABLE_CONSTRAINTS`
+            WHERE table_name IN (
+                'dim_country', 'dim_indicator', 'dim_time',
+                'dim_source', 'dim_pillar', 'fact_food_security'
+            )
+            ORDER BY
+                CASE WHEN table_name LIKE 'dim_%' THEN 1 ELSE 2 END,
+                table_name, constraint_type
+            """
+            df = self.client.query(query).result().to_dataframe(create_bqstorage_client=False)
+            if len(df) > 0:
+                for _, row in df.iterrows():
+                    icon = "[PK]" if row['constraint_type'] == "PRIMARY KEY" else "[FK]"
+                    self.logger.info(
+                        f"  {icon} {row['table_name']:25s} | "
+                        f"{row['constraint_type']:15s} | {row['constraint_name']}"
+                    )
+                pk_count = len(df[df['constraint_type'] == 'PRIMARY KEY'])
+                fk_count = len(df[df['constraint_type'] == 'FOREIGN KEY'])
+                self.logger.info(f"\n  Primary Keys : {pk_count}")
+                self.logger.info(f"  Foreign Keys : {fk_count}")
+                self.logger.info(f"  Total        : {len(df)}")
+            else:
+                self.logger.warning("  [WARN] No constraints found!")
+        except Exception as e:
+            self.logger.error(f"Error validating constraints: {e}")
+
+    def validate_data_load(self):
+        self.logger.info("\n" + "=" * 60)
+        self.logger.info("DATA LOAD VALIDATION — fs_asean_gold")
+        self.logger.info("=" * 60)
+        try:
+            for table in ['dim_country', 'dim_indicator', 'dim_time',
+                          'dim_source', 'dim_pillar', 'fact_food_security']:
+                df = read_from_bigquery(self.client, table, layer='gold')
+                self.logger.info(f"  {table:25s}: {len(df):>10,} rows")
+
+            query = f"""
+            SELECT
+                COUNT(*)                     AS total_facts,
+                COUNT(DISTINCT country_id)   AS unique_countries,
+                COUNT(DISTINCT indicator_id) AS unique_indicators,
+                COUNT(DISTINCT time_id)      AS unique_years,
+                COUNT(DISTINCT source_id)    AS unique_sources,
+                COUNT(DISTINCT pillar_id)    AS unique_pillars
+            FROM `{get_table_id('fact_food_security', layer='gold')}`
+            """
+            stats = self.client.query(query).result().to_dataframe(
+                create_bqstorage_client=False
+            ).iloc[0]
+            self.logger.info(f"\n  Fact Table Summary:")
+            self.logger.info(f"    Total Facts       : {int(stats['total_facts']):>10,}")
+            self.logger.info(f"    Unique Countries  : {int(stats['unique_countries']):>10,}")
+            self.logger.info(f"    Unique Indicators : {int(stats['unique_indicators']):>10,}")
+            self.logger.info(f"    Unique Years      : {int(stats['unique_years']):>10,}")
+            self.logger.info(f"    Unique Sources    : {int(stats['unique_sources']):>10,}")
+            self.logger.info(f"    Unique Pillars    : {int(stats['unique_pillars']):>10,}")
+
+            query_dir = f"""
+            SELECT direction, COUNT(*) AS count
+            FROM `{get_table_id('dim_indicator', layer='gold')}`
+            GROUP BY direction ORDER BY direction
+            """
+            df_dir = self.client.query(query_dir).result().to_dataframe(create_bqstorage_client=False)
+            if len(df_dir) > 0:
+                self.logger.info(f"\n  Direction Distribution:")
+                for _, row in df_dir.iterrows():
+                    self.logger.info(f"    {row['direction']:15s}: {int(row['count']):>5,} indicators")
+
+            self.logger.info("\n  [OK] Validation completed")
+        except Exception as e:
+            self.logger.error(f"Error during validation: {e}")
+            raise
+
+    # ------------------------------------------------------------------
+    # RUN
+    # ------------------------------------------------------------------
+
+    def run(self):
+        """Execute full dimensional model load ke DW layer (Gold)."""
+        self.pipeline_metadata['start_time']   = datetime.now()
+        self.pipeline_metadata['rows_fetched'] = len(self.df_clean)
+
+        self.logger.info("\n" + "=" * 60)
+        self.logger.info("DIMENSIONAL MODEL LOAD — DW (Gold) → fs_asean_gold")
+        self.logger.info("=" * 60)
+
+        # Dimensions
+        self.logger.info("\nLOADING DIMENSION TABLES → fs_asean_gold")
+        self.load_dim_country()
+        self.load_dim_indicator()
+        self.load_dim_time()
+        self.load_dim_source()
+        self.load_dim_pillar()
+
+        # Fact
+        self.logger.info("\nLOADING FACT TABLE → fs_asean_gold")
+        self.load_fact_food_security()
+
+        # Validate
+        self.validate_constraints()
+        self.validate_data_load()
+
+        pipeline_end = datetime.now()
+        duration     = (pipeline_end - self.pipeline_metadata['start_time']).total_seconds()
+        total_loaded = sum(m['rows_loaded'] for m in self.load_metadata.values())
+
+        self.pipeline_metadata.update({
+            'end_time'          : pipeline_end,
+            'duration_seconds'  : duration,
+            'rows_transformed'  : total_loaded,
+            'rows_loaded'       : total_loaded,
+            'execution_timestamp': self.pipeline_metadata['start_time'],
+            'completeness_pct'  : 100.0,
+            'config_snapshot'   : json.dumps({'load_mode': 'full_refresh', 'layer': 'gold'}),
+            'validation_metrics': json.dumps({t: m['status'] for t, m in self.load_metadata.items()}),
+            'table_name'        : 'dimensional_model_pipeline',
+        })
+        try:
+            save_etl_metadata(self.client, self.pipeline_metadata)
+        except Exception as e:
+            self.logger.warning(f"  [WARN] Could not save pipeline metadata: {e}")
+
+        # Summary
+        self.logger.info("\n" + "=" * 60)
+        self.logger.info("DIMENSIONAL MODEL LOAD COMPLETED")
+        self.logger.info("=" * 60)
+        self.logger.info(f"  Dataset  : fs_asean_gold")
+        self.logger.info(f"  Duration : {duration:.2f}s")
+        self.logger.info(f"  Tables   :")
+        for tbl, meta in self.load_metadata.items():
+            icon = "✓" if meta['status'] == 'success' else "✗"
+            self.logger.info(f"    {icon} {tbl:25s}: {meta['rows_loaded']:>10,} rows")
+        self.logger.info(f"\n  Metadata → [AUDIT] etl_metadata")
+        self.logger.info("=" * 60)
+
+
+# =============================================================================
+# AIRFLOW TASK FUNCTIONS  ← sama polanya dengan raw & cleaned layer
+# =============================================================================
+
+def run_dimensional_model():
+    """
+    Airflow task: Load dimensional model dari cleaned_integrated.
+
+    Dipanggil oleh DAG setelah task cleaned_integration_to_silver selesai.
+    """
+    from scripts.bigquery_config import get_bigquery_client
+    client   = get_bigquery_client()
+    df_clean = read_from_bigquery(client, 'cleaned_integrated', layer='silver')
+    loader   = DimensionalModelLoader(client, df_clean)
+    loader.run()
+    print(f"Dimensional model loaded: {len(df_clean):,} source rows processed")
+
+
+# =============================================================================
+# MAIN EXECUTION
+# =============================================================================
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("BIGQUERY DIMENSIONAL MODEL LOAD")
+    print("Kimball DW Architecture")
+    print("  Input  : STAGING (Silver) → cleaned_integrated (fs_asean_silver)")
+    print("  Output : DW      (Gold)   → dim_*, fact_*       (fs_asean_gold)")
+    print("  Audit  : AUDIT            → etl_logs, etl_metadata (fs_asean_audit)")
+    print("=" * 60)
+
+    logger   = setup_logging()
+    client   = get_bigquery_client()
+
+    print("\nLoading cleaned_integrated (fs_asean_silver)...")
+    df_clean = read_from_bigquery(client, 'cleaned_integrated', layer='silver')
+    print(f"  ✓ Loaded       : {len(df_clean):,} rows")
+    print(f"    Columns      : {len(df_clean.columns)}")
+    print(f"    Sources      : {df_clean['source'].nunique()}")
+    print(f"    Indicators   : {df_clean['indicator_standardized'].nunique()}")
+    print(f"    Countries    : {df_clean['country'].nunique()}")
+    print(f"    Year range   : {int(df_clean['year'].min())}–{int(df_clean['year'].max())}")
+    if 'direction' in df_clean.columns:
+        print(f"    Direction    : {df_clean['direction'].value_counts().to_dict()}")
+    else:
+        print(f"    [WARN] direction column not found — run bigquery_cleaned_layer.py first")
+
+    print("\n[1/1] Dimensional Model Load → DW (Gold)...")
+    loader = DimensionalModelLoader(client, df_clean)
+    loader.run()
+
+    print("\n" + "=" * 60)
+    print("✓ DIMENSIONAL MODEL ETL COMPLETED")
+    print("  🥇 DW (Gold) : dim_country, dim_indicator, dim_time,")
+    print("                dim_source, dim_pillar, fact_food_security")
+    print("  📋 AUDIT     : etl_logs, etl_metadata")
+    print("=" * 60)

scripts/bigquery_helpers.py

@@ -1,23 +1,6 @@
 """
 BIGQUERY HELPER FUNCTIONS
 Kimball Data Warehouse Architecture
-
-Layer Assignment (Kimball terminology):
-    RAW     (Bronze) → raw_fao, raw_worldbank, raw_unicef
-    STAGING (Silver) → staging_integrated, cleaned_integrated
-    AUDIT   (Audit)  → etl_logs, etl_metadata
-    DW      (Gold)   → dim_*, fact_food_security, fact_food_security_eligible
-
-Functions:
-    setup_logging()       — Setup file & console logging
-    log_update()          — Audit log ETL ke staging (Silver)
-    save_etl_metadata()   — Save ETL metadata ke staging (Silver), preserve created_at
-    load_to_bigquery()    — Load DataFrame ke layer tertentu
-    read_from_bigquery()  — Read dari layer tertentu
-    truncate_table()      — Hapus semua rows dari table
-    drop_table()          — Drop table dari layer tertentu
-    get_staging_schema()  — Schema staging_integrated
-    get_etl_metadata_schema() — Schema etl_metadata
 """
 
 import pandas as pd
@@ -25,7 +8,7 @@ import logging
 from datetime import datetime
 import pytz
 from google.cloud import bigquery
-from bigquery_config import (
+from scripts.bigquery_config import (
     get_bigquery_client,
     get_table_id,
     table_exists,
@@ -35,16 +18,9 @@ import json
 
 # LOGGING SETUP
 
+logger = logging.getLogger(__name__)
+
 def setup_logging(log_file: str = 'logs/etl_pipeline.log') -> logging.Logger:
-    """
-    Setup logging system untuk tracking eksekusi ETL
-
-    Args:
-        log_file: Path to log file
-
-    Returns:
-        logging.Logger: Configured logger
-    """
     logging.basicConfig(
         level=logging.INFO,
         format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
@@ -55,27 +31,8 @@ def setup_logging(log_file: str = 'logs/etl_pipeline.log') -> logging.Logger:
     )
     return logging.getLogger(__name__)
 
-# ETL AUDIT LOG — STAGING LAYER (Silver)
 
 def ensure_etl_logs_table(client: bigquery.Client):
-    """
-    Buat table etl_logs di STAGING layer (Silver) jika belum ada.
-
-    Kimball context:
-        etl_logs adalah operational/audit table, bukan bagian dari Star Schema.
-        Disimpan di Staging layer karena merupakan output proses ETL,
-        bukan data warehouse final.
-
-    Schema:
-        id            STRING   — unique log ID
-        timestamp     DATETIME — waktu log dibuat
-        layer         STRING   — layer yang diproses (RAW/STAGING/DW)
-        table_name    STRING   — nama table yang diproses
-        update_method STRING   — full_refresh / incremental
-        rows_affected INTEGER  — jumlah rows
-        status        STRING   — success / failed
-        error_message STRING   — pesan error jika gagal
-    """
     if not table_exists(client, 'etl_logs', layer='audit'):
         table_id = get_table_id('etl_logs', layer='audit')
         schema = [
@@ -96,28 +53,6 @@ def ensure_etl_logs_table(client: bigquery.Client):
 def log_update(client: bigquery.Client, layer: str, table_name: str,
                update_method: str, rows_affected: int,
                status: str = 'success', error_msg: str = None):
-    """
-    Catat aktivitas ETL ke etl_logs (STAGING/Silver) untuk audit trail.
-
-    Args:
-        client        : BigQuery client
-        layer         : Layer yang diproses — 'RAW', 'STAGING', atau 'DW'
-        table_name    : Nama table yang diproses
-        update_method : 'full_refresh' atau 'incremental'
-        rows_affected : Jumlah rows yang diproses
-        status        : 'success' atau 'failed'
-        error_msg     : Pesan error jika status='failed'
-
-    Examples:
-        # Log saat load raw data
-        log_update(client, 'RAW', 'raw_fao', 'full_refresh', 5000)
-
-        # Log saat proses staging
-        log_update(client, 'STAGING', 'staging_integrated', 'full_refresh', 12000)
-
-        # Log saat load ke DW
-        log_update(client, 'DW', 'fact_food_security', 'full_refresh', 8000)
-    """
     try:
         ensure_etl_logs_table(client)
 
@@ -133,7 +68,6 @@ def log_update(client: bigquery.Client, layer: str, table_name: str,
             'error_message': error_msg
         }])
 
-        # Hapus timezone untuk BigQuery DATETIME
         log_data['timestamp'] = pd.to_datetime(log_data['timestamp']).dt.tz_localize(None)
         log_data['id']        = log_data['id'].astype(str)
 
@@ -143,42 +77,13 @@ def log_update(client: bigquery.Client, layer: str, table_name: str,
         job.result()
 
     except Exception as e:
-        print(f"  Warning: Failed to write etl_logs [STAGING]: {e}")
+        print(f"  Warning: Failed to write etl_logs [AUDIT]: {e}")
 
-# DATA LOADING TO BIGQUERY
 
 def load_to_bigquery(client: bigquery.Client, df: pd.DataFrame,
                      table_name: str, layer: str = "bronze",
                      write_disposition: str = "WRITE_TRUNCATE",
                      schema: list = None) -> int:
-    """
-    Load DataFrame ke BigQuery table pada layer tertentu.
-
-    Args:
-        client            : BigQuery client
-        df                : DataFrame yang akan di-load
-        table_name        : Nama table tujuan
-        layer             : 'bronze'/'raw', 'silver'/'staging', 'gold'/'dw'
-        write_disposition : WRITE_TRUNCATE (replace) atau WRITE_APPEND (append)
-        schema            : Optional schema (list of SchemaField)
-
-    Returns:
-        int: Jumlah rows yang berhasil di-load
-
-    Examples (Kimball flow):
-        # RAW layer — data mentah dari sumber
-        load_to_bigquery(client, df_fao,     'raw_fao',       layer='bronze')
-        load_to_bigquery(client, df_wb,      'raw_worldbank',  layer='bronze')
-        load_to_bigquery(client, df_unicef,  'raw_unicef',     layer='bronze')
-
-        # STAGING layer — cleaned & integrated
-        load_to_bigquery(client, df_staging, 'staging_integrated', layer='silver')
-
-        # DW layer — Kimball Star Schema
-        load_to_bigquery(client, df_dim,     'dim_country',                layer='gold')
-        load_to_bigquery(client, df_fact,    'fact_food_security',         layer='gold')
-        load_to_bigquery(client, df_elig,    'fact_food_security_eligible', layer='gold')
-    """
     table_id   = get_table_id(table_name, layer)
     job_config = bigquery.LoadJobConfig(
         write_disposition=write_disposition,
@@ -193,36 +98,11 @@ def load_to_bigquery(client: bigquery.Client, df: pd.DataFrame,
     print(f"  ✓ Loaded {table.num_rows:,} rows → [{layer.upper()}] {table_name}")
     return table.num_rows
 
-# DATA READING FROM BIGQUERY
 
 def read_from_bigquery(client: bigquery.Client,
                        table_name: str = None,
                        layer: str = "bronze",
                        query: str = None) -> pd.DataFrame:
-    """
-    Read data dari BigQuery table atau jalankan custom query.
-
-    Args:
-        client     : BigQuery client
-        table_name : Nama table yang akan dibaca
-        layer      : 'bronze'/'raw', 'silver'/'staging', 'gold'/'dw'
-        query      : Custom SQL query (jika diisi, table_name diabaikan)
-
-    Returns:
-        pd.DataFrame: Hasil query
-
-    Examples (Kimball flow):
-        # Baca dari RAW layer
-        df = read_from_bigquery(client, 'raw_fao',              layer='bronze')
-
-        # Baca dari STAGING layer
-        df = read_from_bigquery(client, 'staging_integrated',   layer='silver')
-
-        # Baca dari DW layer
-        df = read_from_bigquery(client, 'fact_food_security',           layer='gold')
-        df = read_from_bigquery(client, 'fact_food_security_eligible',  layer='gold')
-        df = read_from_bigquery(client, 'dim_country',                  layer='gold')
-    """
     if query:
         return client.query(query).result().to_dataframe(create_bqstorage_client=False)
     elif table_name:
@@ -231,17 +111,8 @@ def read_from_bigquery(client: bigquery.Client,
     else:
         raise ValueError("Either table_name or query must be provided")
 
-# TABLE MANAGEMENT
 
 def truncate_table(client: bigquery.Client, table_name: str, layer: str = "bronze"):
-    """
-    Hapus semua rows dari table (kosongkan table, struktur tetap ada).
-
-    Args:
-        client     : BigQuery client
-        table_name : Nama table
-        layer      : 'bronze'/'raw', 'silver'/'staging', 'gold'/'dw'
-    """
     table_id = get_table_id(table_name, layer)
     job      = client.query(f"DELETE FROM `{table_id}` WHERE TRUE")
     job.result()
@@ -249,30 +120,12 @@ def truncate_table(client: bigquery.Client, table_name: str, layer: str = "bronz
 
 
 def drop_table(client: bigquery.Client, table_name: str, layer: str = "bronze"):
-    """
-    Drop table dari BigQuery jika ada.
-
-    Args:
-        client     : BigQuery client
-        table_name : Nama table
-        layer      : 'bronze'/'raw', 'silver'/'staging', 'gold'/'dw'
-    """
     table_id = get_table_id(table_name, layer)
     client.delete_table(table_id, not_found_ok=True)
     print(f"  Dropped [{layer.upper()}] table: {table_name}")
 
-# SCHEMA DEFINITIONS — STAGING LAYER (Silver)
 
 def get_staging_schema() -> list:
-    """
-    Schema untuk staging_integrated table (STAGING/Silver layer).
-
-    Staging table adalah area integrasi data dari semua sumber (FAO, WB, UNICEF)
-    sebelum di-load ke DW layer sebagai Dim & Fact tables.
-
-    Returns:
-        list: List of SchemaField objects
-    """
     return [
         bigquery.SchemaField("source",                 "STRING",  mode="REQUIRED"),
         bigquery.SchemaField("indicator_original",     "STRING",  mode="REQUIRED"),
@@ -286,15 +139,6 @@ def get_staging_schema() -> list:
 
 
 def get_etl_metadata_schema() -> list:
-    """
-    Schema untuk etl_metadata table (STAGING/Silver layer).
-
-    ETL metadata disimpan di Staging layer karena merupakan operational table
-    untuk reproducibility & tracking, bukan bagian Star Schema DW.
-
-    Returns:
-        list: List of SchemaField objects
-    """
     return [
         bigquery.SchemaField("id",                  "STRING",    mode="REQUIRED"),
         bigquery.SchemaField("source_class",        "STRING",    mode="REQUIRED"),
@@ -311,43 +155,17 @@ def get_etl_metadata_schema() -> list:
         bigquery.SchemaField("updated_at",          "TIMESTAMP", mode="REQUIRED"),
     ]
 
-# ETL METADATA — STAGING LAYER (Silver)
-# FIXED: Preserve created_at dari eksekusi pertama
 
 def save_etl_metadata(client: bigquery.Client, metadata: dict):
-    """
-    Save ETL metadata ke etl_metadata table (STAGING/Silver layer).
-
-    Logic created_at vs updated_at:
-        created_at : diambil dari record PERTAMA untuk table_name yang sama
-                     (preserved across runs — untuk reproducibility)
-        updated_at : selalu diperbarui ke waktu eksekusi sekarang
-
-    Args:
-        client   : BigQuery client
-        metadata : Dict berisi informasi eksekusi ETL:
-            table_name           (required)
-            source_class         (required)
-            execution_timestamp
-            duration_seconds
-            rows_fetched
-            rows_transformed
-            rows_loaded
-            completeness_pct
-            config_snapshot      (JSON string)
-            validation_metrics   (JSON string)
-    """
     table_name = metadata.get('table_name', 'unknown')
     table_id   = get_table_id('etl_metadata', layer='audit')
 
-    # Buat table jika belum ada
     if not table_exists(client, 'etl_metadata', layer='audit'):
         schema = get_etl_metadata_schema()
         table  = bigquery.Table(table_id, schema=schema)
         client.create_table(table)
         print(f"  [AUDIT] Created table: etl_metadata")
 
-    # Ambil created_at pertama untuk table ini (preserve across runs)
     check_query = f"""
         SELECT MIN(created_at) AS first_created_at
         FROM `{table_id}`
@@ -373,7 +191,6 @@ def save_etl_metadata(client: bigquery.Client, metadata: dict):
 
     current_time = datetime.now()
 
-    # Generate unique ID
     import hashlib
     record_id = hashlib.md5(
         f"{metadata.get('source_class')}_{table_name}_{current_time.isoformat()}".encode()
@@ -391,15 +208,13 @@ def save_etl_metadata(client: bigquery.Client, metadata: dict):
         'completeness_pct'   : float(metadata.get('completeness_pct', 0)),
         'config_snapshot'    : metadata.get('config_snapshot', '{}'),
         'validation_metrics' : metadata.get('validation_metrics', '{}'),
-        'created_at'         : created_at,    # PRESERVED dari run pertama
-        'updated_at'         : current_time   # SELALU waktu sekarang
+        'created_at'         : created_at,
+        'updated_at'         : current_time
     }])
 
-    # Hapus timezone untuk BigQuery
     for col in ['execution_timestamp', 'created_at', 'updated_at']:
         meta_df[col] = pd.to_datetime(meta_df[col]).dt.tz_localize(None)
 
-    # APPEND ke STAGING layer (Silver)
     job_config = bigquery.LoadJobConfig(write_disposition="WRITE_APPEND")
     job        = client.load_table_from_dataframe(meta_df, table_id, job_config=job_config)
     job.result()
@@ -409,14 +224,3 @@ def save_etl_metadata(client: bigquery.Client, metadata: dict):
     else:
         print(f"etl_metadata — preserved  | created_at : {created_at}")
     print(f"etl_metadata — updated_at : {current_time}")
-
-# INITIALIZE
-
-logger = setup_logging()
-client = get_bigquery_client()
-
-print("BigQuery Helpers Loaded — Kimball DW Architecture")
-print(f"Project          : {CONFIG['bigquery']['project_id']}")
-print(f"Raw     (Bronze) : {CONFIG['bigquery']['dataset_bronze']}")
-print(f"Staging (Silver) : {CONFIG['bigquery']['dataset_silver']}")
-print(f"DW      (Gold)   : {CONFIG['bigquery']['dataset_gold']}")

scripts/bigquery_raw_layer.py

@@ -33,8 +33,8 @@ from pathlib import Path
 from difflib import SequenceMatcher
 import re
 
-from bigquery_config import get_bigquery_client, CONFIG, EXPORTS_DIR, LOGS_DIR, get_table_id
-from bigquery_helpers import (
+from scripts.bigquery_config import get_bigquery_client, CONFIG, EXPORTS_DIR, LOGS_DIR, get_table_id
+from scripts.bigquery_helpers import (
     log_update,
     load_to_bigquery,
     read_from_bigquery,
@@ -42,9 +42,10 @@ from bigquery_helpers import (
     save_etl_metadata,
     get_staging_schema
 )
-from bigquery_datasource import DataSource
+from scripts.bigquery_datasource import DataSource
 from google.cloud import bigquery
 
+
 # INDICATOR MATCHER
 
 class IndicatorMatcher:
@@ -200,7 +201,7 @@ class IndicatorMatcher:
 class FAODataSource(DataSource):
     """
     FAO Food Security Data Source (BigQuery version)
-    FIXED: Menggunakan bulk download karena faostat API butuh autentikasi
+    Menggunakan bulk download karena faostat API butuh autentikasi
     """
 
     def __init__(self, client: bigquery.Client = None):
@@ -447,28 +448,22 @@ class StagingDataIntegration:
         }
 
     def load_raw_data(self) -> Dict[str, pd.DataFrame]:
-        """Load data dari semua tabel RAW layer (Bronze)"""
         raw_data = {}
-
         try:
             raw_data['fao'] = read_from_bigquery(self.client, 'raw_fao', layer='bronze')
         except Exception:
             raw_data['fao'] = pd.DataFrame()
-
         try:
             raw_data['worldbank'] = read_from_bigquery(self.client, 'raw_worldbank', layer='bronze')
         except Exception:
             raw_data['worldbank'] = pd.DataFrame()
-
         try:
             raw_data['unicef'] = read_from_bigquery(self.client, 'raw_unicef', layer='bronze')
         except Exception:
             raw_data['unicef'] = pd.DataFrame()
-
         return raw_data
 
     def clean_value(self, value):
-        """Clean dan convert value ke float"""
         if pd.isna(value):
             return None
         value_str = str(value).strip().replace('<', '').replace('>', '').strip()
@@ -478,18 +473,9 @@ class StagingDataIntegration:
             return None
 
     def process_year_range(self, year_value):
-        """
-        Process year range dan return (year_int, year_range_str)
-        Examples:
-            "2020"      → (2020, "2020")
-            "2020-2021" → (2020, "2020-2021")
-            "2019–2021" → (2020, "2019-2021")
-        """
         if pd.isna(year_value):
             return None, None
-
         year_str = str(year_value).strip().replace('–', '-').replace('—', '-')
-
         if '-' in year_str:
             try:
                 parts = year_str.split('-')
@@ -509,7 +495,6 @@ class StagingDataIntegration:
                 return None, year_str
 
     def truncate_string(self, value, max_length: int) -> str:
-        """Truncate string sesuai varchar constraint"""
         if pd.isna(value):
             return ''
         s = str(value).strip()
@@ -519,7 +504,6 @@ class StagingDataIntegration:
                                indicator_orig_col: str, indicator_std_col: str,
                                country_col: str, year_col: str, value_col: str,
                                unit_col: str = None) -> pd.DataFrame:
-        """Standardize dataframe ke schema staging_integrated"""
         if df.empty:
             return pd.DataFrame()
 
@@ -543,10 +527,9 @@ class StagingDataIntegration:
         })
 
     def standardize_schema(self, raw_data: Dict[str, pd.DataFrame]) -> pd.DataFrame:
-        """Standardize schema dari semua sumber data"""
         integrated_data = []
 
-        # FAO — deteksi kolom (nama asli atau sudah di-rename)
+        # FAO
         if not raw_data['fao'].empty:
             df = raw_data['fao'].copy()
             integrated_data.append(self.standardize_dataframe(
@@ -590,11 +573,9 @@ class StagingDataIntegration:
 
         df_integrated = pd.concat(integrated_data, ignore_index=True)
 
-        # Final type conversion
         df_integrated['year']  = pd.to_numeric(df_integrated['year'],  errors='coerce')
         df_integrated['value'] = pd.to_numeric(df_integrated['value'], errors='coerce')
 
-        # Enforce varchar constraints
         for col, max_len in [('source', 20), ('country', 100), ('indicator_original', 255),
                               ('indicator_standardized', 255), ('year_range', 20), ('unit', 20)]:
             df_integrated[col] = df_integrated[col].astype(str).apply(
@@ -606,7 +587,6 @@ class StagingDataIntegration:
         ).reset_index(drop=True)
 
     def validate_data(self, df: pd.DataFrame) -> Dict:
-        """Validate data dan return metrics"""
         validation = {
             'total_rows'      : int(len(df)),
             'total_columns'   : int(len(df.columns)),
@@ -621,15 +601,12 @@ class StagingDataIntegration:
                 'max'         : int(df['year'].max()) if not df['year'].isnull().all() else None,
                 'unique_years': int(df['year'].nunique())
             }
-
         if 'source' in df.columns:
             validation['source_breakdown'] = {
                 str(k): int(v) for k, v in df['source'].value_counts().to_dict().items()
             }
-
         if 'indicator_standardized' in df.columns:
             validation['unique_indicators'] = int(df['indicator_standardized'].nunique())
-
         if 'country' in df.columns:
             validation['unique_countries'] = int(df['country'].nunique())
 
@@ -645,21 +622,15 @@ class StagingDataIntegration:
         return validation
 
     def save_to_staging(self, df: pd.DataFrame):
-        """Save data ke staging_integrated table di STAGING layer (Silver)"""
         try:
             schema = get_staging_schema()
-
             load_to_bigquery(
-                self.client,
-                df,
-                self.staging_table,
-                layer='silver',               # → fs_asean_silver
+                self.client, df, self.staging_table,
+                layer='silver',
                 write_disposition="WRITE_TRUNCATE",
                 schema=schema
             )
-
             log_update(self.client, 'STAGING', self.staging_table, 'full_refresh', len(df))
-
         except Exception as e:
             print(f"save_to_staging FAILED: {type(e).__name__}: {e}")
             log_update(self.client, 'STAGING', self.staging_table, 'full_refresh', 0,
@@ -667,7 +638,6 @@ class StagingDataIntegration:
             raise
 
     def run(self) -> pd.DataFrame:
-        """Run staging integration process"""
         self.metadata['start_time'] = datetime.now()
 
         try:
@@ -703,7 +673,6 @@ class StagingDataIntegration:
 
             save_etl_metadata(self.client, self.metadata)
 
-            # Summary
             print(f"  ✓ Staging Integration completed: {len(df_integrated):,} rows")
             print(f"    Duration    : {self.metadata['duration_seconds']:.2f}s")
             if 'source_breakdown' in validation:
@@ -725,7 +694,6 @@ class StagingDataIntegration:
             print(f"    - country max length             : {schema_val['country_max_length']}/100")
             print(f"    - year_range max length          : {schema_val['year_range_max_length']}/20")
             print(f"    - unit max length                : {schema_val['unit_max_length']}/20")
-
             print(f"\n    Metadata → [AUDIT] etl_metadata")
 
             return df_integrated
@@ -734,6 +702,7 @@ class StagingDataIntegration:
             self.logger.error(f"Staging integration failed: {str(e)}")
             raise
 
+
 # MAIN EXECUTION
 
 if __name__ == "__main__":
@@ -745,11 +714,9 @@ if __name__ == "__main__":
     logger = setup_logging()
     client = get_bigquery_client()
 
-    # ── FAO ──────────────────────────────────────────────────────────────────
     print("\n[1/4] Loading FAO Food Security Data → RAW (Bronze)...")
     fao_source = FAODataSource(client)
     df_fao     = fao_source.run()
-
     print(f"  ✓ raw_fao: {len(df_fao):,} rows")
     print(f"    Indicators : {df_fao['indicator'].nunique()}")
     print(f"    Countries  : {df_fao['country'].nunique()}")
@@ -757,28 +724,23 @@ if __name__ == "__main__":
 
     fao_indicators = df_fao['indicator'].unique()
 
-    # ── World Bank ────────────────────────────────────────────────────────────
     print("\n[2/4] Loading World Bank Data → RAW (Bronze)...")
     wb_source = WorldBankDataSource(client, list(fao_indicators))
     df_wb     = wb_source.run()
-
     print(f"  ✓ raw_worldbank: {len(df_wb):,} rows")
     print(f"    Matched indicators : {df_wb['indicator_fao'].nunique()}")
     print(f"    Countries          : {df_wb['country'].nunique()}")
     if len(df_wb) > 0:
         print(f"    Year range         : {df_wb['year'].min()}–{df_wb['year'].max()}")
 
-    # ── UNICEF ────────────────────────────────────────────────────────────────
     print("\n[3/4] Loading UNICEF Data → RAW (Bronze)...")
     unicef_source = UNICEFDataSource(client, list(fao_indicators))
     df_unicef     = unicef_source.run()
-
     print(f"  ✓ raw_unicef: {len(df_unicef):,} rows")
     if len(df_unicef) > 0:
         print(f"    Matched indicators : {df_unicef['indicator_fao'].nunique()}")
         print(f"    Countries          : {df_unicef['country'].nunique()}")
 
-    # ── Staging Integration ───────────────────────────────────────────────────
     print("\n[4/4] Staging Integration → STAGING (Silver)...")
     staging    = StagingDataIntegration(client)
     df_staging = staging.run()
@@ -790,6 +752,7 @@ if __name__ == "__main__":
     print(f"AUDIT            : etl_logs, etl_metadata")
     print("=" * 60)
 
+
 # AIRFLOW TASK FUNCTIONS
 
 def run_verify_connection():

scripts/test_data.py

@@ -0,0 +1,37 @@
+import requests
+import zipfile
+import io
+import pandas as pd
+
+def run_fao_test():
+    from scripts.bigquery_config import get_bigquery_client
+    from scripts.bigquery_helpers import load_to_bigquery, read_from_bigquery
+
+    print("--- MEMULAI TEST LOAD FAO KE BIGQUERY ---")
+
+    # 1. Extract
+    url = "https://bulks-faostat.fao.org/production/Food_Security_Data_E_All_Data_(Normalized).zip"
+    response = requests.get(url, timeout=120)
+    with zipfile.ZipFile(io.BytesIO(response.content)) as z:
+        csv_name = [f for f in z.namelist() if f.endswith('.csv')][0]
+        df = pd.read_csv(z.open(csv_name), encoding='latin-1')
+
+    # Ambil 5 baris teratas
+    df_top5 = df.head(5)
+
+    print("HASIL 5 DATA TERATAS:")
+    print("====================================================")
+    print(df_top5.to_string(index=False))
+    print("====================================================")
+
+    # 2. Load ke BigQuery
+    client = get_bigquery_client()
+    load_to_bigquery(client, df_top5, "raw_fao_test", layer="bronze", write_disposition="WRITE_TRUNCATE")
+    print(f"Total data yang berhasil di-load: {len(df_top5)} baris.")
+
+    # 3. Verify
+    df_check = read_from_bigquery(client, "raw_fao_test", layer="bronze")
+    print(f"Verify dari BigQuery: {len(df_check)} baris tersimpan.")
+
+if __name__ == "__main__":
+    run_fao_test()