diff --git a/airflow/dags/Bugs_DAG.py b/airflow/dags/Bugs_DAG.py
index 3e9dc9e..7882a72 100644
--- a/airflow/dags/Bugs_DAG.py
+++ b/airflow/dags/Bugs_DAG.py
@@ -119,7 +119,7 @@ def upload_to_s3(**kwargs):
     "bugs_chart_dag",
     default_args=default_args,
     schedule_interval="10 0 * * *",  # 매일 00:10 실행
-    catchup=True,
+    catchup=False,
 ) as dag:
 
     get_spotify_token_task = PythonOperator(
diff --git a/airflow/dags/Flo_DAG.py b/airflow/dags/Flo_DAG.py
index 769513a..63a7b20 100644
--- a/airflow/dags/Flo_DAG.py
+++ b/airflow/dags/Flo_DAG.py
@@ -118,7 +118,7 @@ def upload_to_s3(**kwargs):
     "flo_chart_dag",
     default_args=default_args,
     schedule_interval="20 0 * * *",  # 매일 00:20 실행
-    catchup=True,
+    catchup=False,
 ) as dag:
 
     get_spotify_token_task = PythonOperator(
diff --git a/airflow/dags/Genie_DAG.py b/airflow/dags/Genie_DAG.py
index ae92f55..c62ff25 100644
--- a/airflow/dags/Genie_DAG.py
+++ b/airflow/dags/Genie_DAG.py
@@ -118,7 +118,7 @@ def upload_to_s3(**kwargs):
     "genie_chart_dag",
     default_args=default_args,
     schedule_interval="30 0 * * *",  # 매일 00:30 실행
-    catchup=True,
+    catchup=False,
 ) as dag:
 
     get_spotify_token_task = PythonOperator(
diff --git a/airflow/dags/Melon_DAG.py b/airflow/dags/Melon_DAG.py
index c7e4fce..da250bb 100644
--- a/airflow/dags/Melon_DAG.py
+++ b/airflow/dags/Melon_DAG.py
@@ -117,7 +117,7 @@ def upload_to_s3(**kwargs):
     "melon_chart_dag",
     default_args=default_args,
     schedule_interval="0 1 * * *",  # 매일 01:00 실행
-    catchup=True,
+    catchup=False,
 ) as dag:
 
     get_spotify_token_task = PythonOperator(
diff --git a/airflow/dags/Vibe_DAG.py b/airflow/dags/Vibe_DAG.py
index de30610..4b35f41 100644
--- a/airflow/dags/Vibe_DAG.py
+++ b/airflow/dags/Vibe_DAG.py
@@ -114,7 +114,7 @@ def upload_to_s3(**kwargs):
     "vibe_chart_dag",
     default_args=default_args,
     schedule_interval="45 0 * * *",  # 매일 00:45 실행
-    catchup=True,
+    catchup=False,
 ) as dag:
 
     get_spotify_token_task = PythonOperator(
diff --git a/airflow/dags/domestic_music_chart_elt.py b/airflow/dags/domestic_music_chart_elt.py
new file mode 100644
index 0000000..ad6d0be
--- /dev/null
+++ b/airflow/dags/domestic_music_chart_elt.py
@@ -0,0 +1,206 @@
+import time
+from datetime import timedelta
+
+from airflow import DAG
+from airflow.operators.dummy import DummyOperator
+from airflow.operators.python import PythonOperator
+from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator
+from airflow.utils.dates import days_ago
+
+
+def wait_one_minute():
+    time.sleep(60)
+
+
+default_args = {
+    "owner": "airflow",
+    "start_date": days_ago(1),
+    "retries": 1,
+    "retry_delay": timedelta(minutes=5),
+}
+
+with DAG(
+    "domestic_music_chart_dashboard_elt",
+    default_args=default_args,
+    schedule_interval="@daily",
+    catchup=False,
+) as dag:
+
+    # 시작 Dummy 태스크
+    start = DummyOperator(task_id="start")
+
+    # Step 1: 최신 데이터만 추출하여 adhoc 스키마에 저장 (컬럼 "DATE" 사용)
+    clean_music_chart = SnowflakeOperator(
+        task_id="clean_music_chart",
+        snowflake_conn_id="snowflake_conn",
+        sql="""
+            CREATE OR REPLACE TABLE s4tify.adhoc.music_chart_cleaned AS
+            SELECT
+                rank, title, artist, genre, lastpos, image, peakpos, isnew, source, "DATE" AS time_date
+            FROM s4tify.raw_data.music_charts
+            WHERE "DATE" = (SELECT MAX("DATE") FROM s4tify.raw_data.music_charts);
+        """,
+    )
+
+    # Step 2: 1분 대기 (데이터 저장 후 안정적 처리 위해)
+    wait_task = PythonOperator(
+        task_id="wait_one_minute",
+        python_callable=wait_one_minute,
+    )
+    # Step 3: 대시보드용 ELT SQL 태스크들
+
+    # 3-1. 장르별 인기곡 트렌드 분석 (수정됨)
+    genre_trend_analysis = SnowflakeOperator(
+        task_id="genre_trend_analysis",
+        snowflake_conn_id="snowflake_conn",
+        sql="""
+            CREATE OR REPLACE TABLE s4tify.analytics.genre_trend_analysis AS
+            SELECT
+                genre_flattened.value::string AS genre,
+                COUNT(DISTINCT title) AS total_songs,
+                AVG(rank) AS avg_rank
+            FROM s4tify.adhoc.music_chart_cleaned,
+            LATERAL FLATTEN(input => parse_json(REPLACE(genre, '''', '\"'))) AS genre_flattened
+            GROUP BY genre_flattened.value
+            ORDER BY avg_rank;
+        """,
+    )
+
+    # 3-2. 아티스트별 최고 순위 및 평균 순위 분석
+    artist_performance = SnowflakeOperator(
+        task_id="artist_performance",
+        snowflake_conn_id="snowflake_conn",
+        sql="""
+            CREATE OR REPLACE TABLE s4tify.analytics.artist_performance AS
+            SELECT
+                artist,
+                COUNT(DISTINCT title) AS total_songs,  -- 중복 제거
+                MIN(rank) AS best_rank,
+                AVG(rank) AS avg_rank
+            FROM s4tify.adhoc.music_chart_cleaned
+            WHERE time_date = (SELECT MAX(time_date) FROM s4tify.adhoc.music_chart_cleaned)  -- 최신 데이터만 사용
+            GROUP BY artist
+            ORDER BY best_rank;
+        """,
+    )
+
+    # 3-3. 신곡(NEW) 현황 분석
+    new_songs_analysis = SnowflakeOperator(
+        task_id="new_songs_analysis",
+        snowflake_conn_id="snowflake_conn",
+        sql="""
+            CREATE OR REPLACE TABLE s4tify.analytics.new_songs_analysis AS
+            SELECT
+                COUNT(*) AS total_songs,
+                SUM(CASE WHEN isnew = TRUE THEN 1 ELSE 0 END) AS new_songs,
+                ROUND(100 * SUM(CASE WHEN isnew = TRUE THEN 1 ELSE 0 END) / COUNT(*), 2) AS new_song_percentage
+            FROM s4tify.adhoc.music_chart_cleaned;
+        """,
+    )
+
+    # 3-4. TOP 10 곡의 안정성 분석 (평균/최대 유지 기간)
+    top10_stability = SnowflakeOperator(
+        task_id="top10_stability",
+        snowflake_conn_id="snowflake_conn",
+        sql="""
+            CREATE OR REPLACE TABLE s4tify.analytics.top10_stability AS
+            WITH top10 AS (
+                SELECT title, COUNT(*) AS weeks_on_chart
+                FROM s4tify.raw_data.music_charts
+                WHERE rank <= 10
+                GROUP BY title
+            )
+            SELECT
+                AVG(weeks_on_chart) AS avg_weeks_top10,
+                MAX(weeks_on_chart) AS max_weeks_top10
+            FROM top10;
+        """,
+    )
+
+    # 3-5. 차트 1위 곡의 주간 유지 기간 분석
+    no1_song_duration = SnowflakeOperator(
+        task_id="no1_song_duration",
+        snowflake_conn_id="snowflake_conn",
+        sql="""
+            CREATE OR REPLACE TABLE s4tify.analytics.no1_song_duration AS
+            WITH no1_songs AS (
+                SELECT
+                    LOWER(title) AS title,
+                    YEAR("DATE") AS year,
+                    WEEKOFYEAR("DATE") AS week
+                FROM (
+                    SELECT DISTINCT LOWER(title) as title, "DATE"
+                    FROM s4tify.raw_data.music_charts
+                    WHERE rank = 1
+                ) unique_no1
+                GROUP BY LOWER(title), YEAR("DATE"), WEEKOFYEAR("DATE")
+            )
+            SELECT
+                title,
+                COUNT(*) AS total_weeks_at_no1
+            FROM no1_songs
+            GROUP BY title
+            HAVING COUNT(*) >= 2
+            ORDER BY total_weeks_at_no1 DESC;
+        """,
+    )
+
+    # 3-6. 랭킹 상승/하락 곡 분석
+    rank_change_analysis = SnowflakeOperator(
+        task_id="rank_change_analysis",
+        snowflake_conn_id="snowflake_conn",
+        sql="""
+            CREATE OR REPLACE TABLE s4tify.analytics.rank_change_analysis AS
+            WITH prev_chart AS (
+                SELECT title, artist, rank
+                FROM (
+                    SELECT title, artist, rank, "DATE",
+                        ROW_NUMBER() OVER (PARTITION BY title, artist ORDER BY "DATE" DESC) AS rn
+                    FROM s4tify.raw_data.music_charts
+                    WHERE "DATE" = (
+                        SELECT MAX("DATE") FROM s4tify.raw_data.music_charts
+                        WHERE "DATE" < (SELECT MAX("DATE") FROM s4tify.raw_data.music_charts)
+                    )
+                )
+                WHERE rn = 1  -- 중복 제거: 가장 최근 데이터만 선택
+            ),
+            latest_chart AS (
+                SELECT title, artist, rank
+                FROM (
+                    SELECT title, artist, rank, "DATE",
+                        ROW_NUMBER() OVER (PARTITION BY title, artist ORDER BY "DATE" DESC) AS rn
+                    FROM s4tify.raw_data.music_charts
+                    WHERE "DATE" = (SELECT MAX("DATE") FROM s4tify.raw_data.music_charts)
+                )
+                WHERE rn = 1  -- 중복 제거: 가장 최근 데이터만 선택
+            )
+            SELECT
+                l.title,
+                l.artist,
+                p.rank AS prev_rank,
+                l.rank AS current_rank,
+                (p.rank - l.rank) AS rank_change
+            FROM latest_chart l
+            LEFT JOIN prev_chart p ON l.title = p.title AND l.artist = p.artist
+            WHERE p.rank IS NOT NULL AND l.rank IS NOT NULL  -- NULL 값 제거
+            ORDER BY rank_change DESC;
+        """,
+    )
+
+    # 종료 Dummy 태스크
+    end = DummyOperator(task_id="end")
+
+    # DAG 실행 순서
+    start >> clean_music_chart >> wait_task
+    (
+        wait_task
+        >> [
+            genre_trend_analysis,
+            artist_performance,
+            new_songs_analysis,
+            top10_stability,
+            no1_song_duration,
+            rank_change_analysis,
+        ]
+        >> end
+    )