-
Notifications
You must be signed in to change notification settings - Fork 0
/
etl.py
62 lines (50 loc) · 1.56 KB
/
etl.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
import configparser
import psycopg2
import pandas as pd
from sql_queries import copy_table_queries, insert_table_queries, analysis_queries
def load_staging_tables(cur, conn):
"""
load data from S3 to staging tables in database
:param cur: cursor of connection
:param conn: connection to database
:return: None
"""
for query in copy_table_queries:
cur.execute(query)
conn.commit()
def insert_tables(cur, conn):
"""
move data from staging tables to star schema's tables
:param cur: cursor of connection
:param conn: connection to database
:return: None
"""
for query in insert_table_queries:
cur.execute(query)
conn.commit()
def get_most_played_song(cur, conn):
"""
execute most played song query
:param cur: cursor of connection
:param conn: connection to database
:return: dataframe of song most played
"""
query = analysis_queries[0]
print(query)
cur.execute(query)
records = cur.fetchall()
df_output = pd.DataFrame(list(records), columns=["song_id", "title", "nums", "artist_name", "year", "duration"])
conn.commit()
return df_output
def main():
config = configparser.ConfigParser()
config.read('dwh.cfg')
conn = psycopg2.connect("host={} dbname={} user={} password={} port={}".format(*config['CLUSTER'].values()))
cur = conn.cursor()
load_staging_tables(cur, conn)
insert_tables(cur, conn)
df_output = get_most_played_song(cur, conn)
print(df_output)
conn.close()
if __name__ == "__main__":
main()