Exemplo de script de ETL do Glue para carregar dados da tabela Tulip

import sys import pandas as pd import numpy as np import requests import json import boto3 from botocore.exceptions import ClientError from sqlalchemy import create\_engine import sqlalchemy as sa from sqlalchemy.engine.url import URL import psycopg2 from datetime import datetime import logging logger = logging.getLogger()


table\_id = 'aKzvoscgHCyd2CRu3\_DEFAULT' def get\_secret(secret\_name, region\_name): # Criar um cliente do Gerenciador de segredos session = boto3.session.Session() client = session.client( service\_name='secretsmanager', region\_name=region\_name ) try: get\_secret\_value\_response = client.get\_secret\_value( SecretId=secret\_name ) except ClientError as e: # Para obter uma lista das exceções lançadas, consulte # https://docs.aws.amazon.com/secretsmanager/latest/apireference/API\_GetSecretValue.html raise e return json.loads(get\_secret\_value\_response['SecretString'])


redshift\_credentials = get\_secret(secret\_name='tulip\_redshift', region\_name='us-east-1') api\_credentials = get\_secret(secret\_name='[INSTANCE].tulip.co-API-KEY', region\_name='us-east-1')


# Criar mecanismo SQL


url = URL.create( drivername='postgresql', host=redshift\_credentials['host'], port=redshift\_credentials['port'], database=redshift\_credentials['dbname'], username=redshift\_credentials['username'], password=redshift\_credentials['password'] ) engine = sa.create\_engine(url)


header = {'Authorization' : api\_credentials['auth\_header']} base\_url = 'https://william.tulip.co/api/v3'


offset = 0 function = f'/tables/{table\_id}/records?limit=100&offset={offset}&includeTotalCount=false&filterAggregator=all' r = requests.get(base\_url+function, headers=header) df = pd.DataFrame(r.json()) length = len(r.json())


while length > 0: offset += 100 function = f'/tables/{table\_id}/records?limit=100&offset={offset}&includeTotalCount=false&filterAggregator=all' r = requests.get(base\_url+function, headers=header) length = len(r.json()) df\_append = pd.DataFrame(r.json()) df = pd.concat([df, df\_append], axis=0)


# capturar registro de data e hora


now = datetime.now() df['datetime\_updated'] = now


# gravar no Redshift


df.to\_sql('station\_activity\_from\_glue', engine, schema='product\_growth', index=False,if\_exists='replace')