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etl.py
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from time import time
import configparser
import psycopg2 # to import the Postgresql python driver
from sql_queries import copy_table_queries, insert_table_queries # import SQL queries lists from sql_queries.py
def load_staging_tables(cur, conn):
"""
Load the staging_tables by executing SQL copy commands
Parameters :
cur : Allows Python code to execute PostgreSQL command in a database session
conn : The connection to the database
Returns:
staging_events & song_events tables loaded with JSON data in the S3 Bucket
"""
for query in copy_table_queries:
t0=time()
cur.execute(copy_table_queries[query])
conn.commit()
copyTime=time()-t0
print(" {} is done in : {} sec\n".format(query,copyTime))
def insert_tables(cur, conn):
"""
Insert the appropriate data int the final tables by executing SQL insert commands
Parameters :
cur : Allows Python code to execute PostgreSQL command in a database session
conn : The connection to the database
Returns:
Dimensional& fact tables loaded with data from staging tables
"""
for query in insert_table_queries:
t0=time()
cur.execute(insert_table_queries[query])
conn.commit()
copyTime=time()-t0
print(" {} is done in : {} sec\n".format(query,copyTime))
def main():
"""
Initiate a configparser instance and read the configuration file 'redshift-configuration.cfg'
Establich the connection to the postgres database in S3 by providing host, database name and credentials in 'redshift-configuration.cfg'
Instanciate a cursor to allow Python code to execute PostgreSQL command in the database session
Call load_staging_tables & insert_tables create above
Close the connection.
"""
config = configparser.ConfigParser()
config.read('redshift-configuration.cfg')
conn = psycopg2.connect("host={} dbname={} user={} password={} port={}".format(*config['DATABASE_CONFIGURATION'].values()))
cur = conn.cursor()
load_staging_tables(cur, conn)
insert_tables(cur, conn)
conn.close()
if __name__ == "__main__":
main()