Transform data from sources using the Python SDK

import sys
import pandas as pd
import logging

from forepaas.dwh import connect
from forepaas.dwh import bulk_insert

logger = logging.getLogger(__name__)
def customfunc(event):
    try:
        logger.notice("Begin function")

        # make connection to the default dataset
        cn = connect("dwh/default_dataset/")

        # option 1 : extract data from the table with no SQL required
        df = cn.select("stations_rides")

        # option 2 : extract data with custom SQL
        df = cn.query("SELECT station_id, date, rides, station_name FROM stations_rides")

        # perform your custom transform in the dataframe
        df.loc[df["station_name"] == 'Harlem-Lake', "rides"] = 0

        # reinsert your dataframe in the destination table
        stats = bulk_insert(cn, "stations_rides", df) 

        # show insertion statistics (if DBMS compatible) 
        logger.info(stats)

        # delete rows where station name is "Davis"
        cn.delete("stations_rides", {"station_name":"Davis"})

        # update rows set rides to 0 where station_id=40040
        cn.update("stations_rides", {"rides":0}, {"station_id":40040})

        # when finished, disconnect cn
        del cn    
        logger.notice("END function")
    except Exception as err: 
        raise Exception("err:{} L:{}".format(err,sys.exc_info()[2].tb_lineno))

import sys
import pandas as pd
from logging import getLogger

from forepaas.dwh import connect
from forepaas.dwh import bulk_insert

logger = getLogger(__name__)
def extract_func(event):
    try:
        # we get data from a bucket and we will archive them in another bucket
        bucket_source_name = "your_source_bucket_name_here"
        bucket_archives_name = "your_source_bucket_name_here"

        # create a connector to handle bucket   
        bucket_connector = connect("data_store/{}".format(bucket_source_name))

        # list files from bucket
        files = bucket_connector.list()

        # retrieve a file from Data Store bucket to temporary local folder
        bucket_filepath = "stations_rides.csv"
        local_filepath = "/tmp/stations_rides.csv"
        bucket_connector.fget(bucket_filepath, local_filepath)

        # read then transform the file as you need
        # here the date column format is simply adjusted for compatibility reasons 
        df = pd.read_csv(local_filepath, sep=';')
        df['date'] = pd.to_datetime(df['date'])

        # load the dataframe into a project table named 'raw_file'
        cn = connect("dwh/default_dataset/")
        bulk_insert(cn, "stations_rides_artur", df)
        del cn
        
        # option 1 : copy the file into the archives bucket
        bucket_archive_filepath = "archives/stations_rides.csv"
        bucket_connector.fcopy_to(bucket_archives_name, bucket_archive_filepath, bucket_filepath)
       
        # option 2 : put a file into the archives
        bucket_archives = connect("data_store/{}".format(bucket_archives_name))
        bucket_archives.fput(bucket_archive_filepath, local_filepath)
        del bucket_archives

        # delete file from source bucket
        bucket_connector.delete(bucket_filepath)

        # disconnect from datastore
        del bucket_connector
    except Exception as err: 
        raise Exception("err:{} L:{}".format(err,sys.exc_info()[2].tb_lineno))

from forepaas.dwh import connect

data_store = connect('data_store')

# Get bucket and upload image from URL to path uploads/test.jpg.
# And finally get the image from the bucket
bucket_test = data_store.get_bucket('test')

lists = bucket_test.list(recursive=True)

bucket_test.put_request("https://i.stack.imgur.com/r8jTK.jpg", path='uploads/test.jpg')
data = bucket.get('hello/test.jpg')

# Create a bucket if it does not already exists
if data_store.bucket_exists('test-exists') is False:
    data_store.create_bucket('test-exists')

# Connect directly to the bucket test and remove the file
bucket_test2 = connect('data_store/test')
bucket_test2.delete('hello/test.jpg')

import sys
from forepaas.dwh.connect import connect
from forepaas.dwh import bulk_insert
import logging

logger = logging.getLogger(__name__)

def customfunc(event):
    try:
        # here we are connecting to a source named 'chicago_files'
        source_address = "dwh/chicago_files_artur/"

        # specify unsupported filename from list of files in source
        filename_w_extension = "stations_rides.csv"

        # connecto to file toget the address 
        source_file_connector = connect(source_address + filename_w_extension) 

        # connect to file directly
        file_address = source_file_connector.get()
        file_connector = connect(file_address) 

        # Extract and treat the file so it is usable
        df = file_connector.extract(return_type='dataframe') 

        # Treat the data
        # - - - - 

        # connect to the Lakehouse Manager
        dm_connector = connect("dwh/default_dataset/")

        # insert into an existing destination table
        stats = bulk_insert(dm_connector, "chicago_calendar_full", df) 

        logger.info(stats)

        # disconnect from datastore and remote source
        del source_connector
        del dm_connector

    except Exception as err:
            raise Exception(f"err:{err} L:{sys.exc_info()[2].tb_lineno}")