Snowflake database pipeline, task, procedure, etc sample

 //create or replace database scd1db

CREATE OR REPLACE STORAGE INTEGRATION SCD1_INT

  TYPE = EXTERNAL_STAGE

  STORAGE_PROVIDER = 'S3'

  ENABLED = TRUE

  STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::848922562186:role/scd1role'

  STORAGE_ALLOWED_LOCATIONS = ('s3://scd1databucket-ambi/data/');

// to describe the database integration details between S3 bucket to snowflake

  DESC INTEGRATION SCD1_INT;

  CREATE OR REPLACE STAGE SCD1_DB.PUBLIC.SCD1_STAGE

STORAGE_INTEGRATION = SCD1_INT

URL= 's3://scd1databucket-ambi/data/';

CREATE OR REPLACE PIPE SCD1_DB.PUBLIC.SCD1PIPE 

AUTO_INGEST = TRUE AS

COPY INTO SCD1_DB.PUBLIC.CUSTOMER_SOURCE

FROM 

(

SELECT

$1 AS CUSTOMERNAME,

$2 AS PHONE,

$3 ASADDRESSLINE1,

$4 AS ADDRESSLINE2,

$5 AS CITY,

$6 AS STATE,

$7 AS POSTALCODE,

$8 AS COUNTRY,

$9 AS TERRITORY,

$10 AS CONTACTFIRSTNAME,

$11 AS CONTACTLASTNAME

FROM @SCD1_DB.PUBLIC.SCD1_STAGE)

FILE_FORMAT = (TYPE = 'CSV' SKIP_HEADER=1 FIELD_OPTIONALLY_ENCLOSED_BY ='"');

USE SCHEMA SCD1_DB.PUBLIC;

SHOW PIPES;

USE ROLE ACCOUNTADMIN;

 

USE WAREHOUSE COMPUTE_WH;

 

USE SCHEMA SCD1_DB.PUBLIC;

CREATE OR REPLACE STREAM SCD1_DB.PUBLIC.CUSTOMER_STREAM

ON TABLE SCD1_DB.PUBLIC.CUSTOMER_SOURCE

APPEND_ONLY = TRUE;

SELECT * FROM SCD1_DB.PUBLIC.CUSTOMER_STREAM;

CREATE OR REPLACE TABLE SCD1_DB.PUBLIC.CUSTOMER

(

CONTACTFIRSTNAME STRING,

CONTACTLASTNAME STRING,

CUSTOMERNAME STRING,

PHONE STRING,

ADDRESSLINE1 STRING,

ADDRESSLINE2 STRING,

CITY STRING,

STATE STRING,

POSTALCODE STRING,

COUNTRY STRING,

TERRITORY STRING,

INSERT_DTS TIMESTAMP(6),

UPDATE_DTS TIMESTAMP(6)

);

CREATE OR REPLACE PROCEDURE SCD1_DB.PUBLIC.CUSTOMER_SP()

RETURNS VARCHAR(50)

LANGUAGE JAVASCRIPT

EXECUTE AS CALLER

AS

$$

try {

 

//Create statement BEGIN, Begins a transaction in the current session

 

snowflake.execute({sqlText:`BEGIN TRANSACTION;`});

 

//load data from Customer Stream to a temp table

    

snowflake.execute({sqlText:`CREATE OR REPLACE TEMPORARY TABLE SCD1_DB.PUBLIC.CUSTOMER_TEMP

AS

SELECT

CONTACTFIRSTNAME,

CONTACTLASTNAME,

CUSTOMERNAME,

PHONE,

ADDRESSLINE1,

ADDRESSLINE2,

CITY,

STATE,

POSTALCODE,

COUNTRY,

TERRITORY,

CURRENT_TIMESTAMP(6) AS INSERT_DTS,

CURRENT_TIMESTAMP(6) AS UPDATE_DTS

FROM 

SCD1_DB.PUBLIC.CUSTOMER_STREAM;`});

    

//Perfom the required SCD1 logic on the Customer Target table based on the primary column

 

snowflake.execute({sqlText:`MERGE INTO SCD1_DB.PUBLIC.CUSTOMER TGT

USING SCD1_DB.PUBLIC.CUSTOMER_TEMP TMP

ON TGT.CONTACTFIRSTNAME = TMP.CONTACTFIRSTNAME

AND TGT.CONTACTLASTNAME = TMP.CONTACTLASTNAME

 

WHEN MATCHED THEN UPDATE SET    

TGT.CUSTOMERNAME = TMP.CUSTOMERNAME,

TGT.PHONE = TMP.PHONE,

TGT.ADDRESSLINE1 = TMP.ADDRESSLINE1,

TGT.ADDRESSLINE2 = TMP.ADDRESSLINE2,

TGT.CITY = TMP.CITY,

TGT.STATE = TMP.STATE,

TGT.POSTALCODE = TMP.POSTALCODE,

TGT.COUNTRY = TMP.COUNTRY,

TGT.TERRITORY = TMP.TERRITORY,

TGT.UPDATE_DTS = TMP.UPDATE_DTS

            

WHEN NOT MATCHED THEN INSERT 

(

CONTACTFIRSTNAME,

CONTACTLASTNAME,

CUSTOMERNAME,

PHONE,

ADDRESSLINE1,

ADDRESSLINE2,

CITY,

STATE,

POSTALCODE,

COUNTRY,

TERRITORY,

INSERT_DTS,

UPDATE_DTS

)

VALUES 

(

TMP.CONTACTFIRSTNAME,

TMP.CONTACTLASTNAME,

TMP.CUSTOMERNAME,

TMP.PHONE,

TMP.ADDRESSLINE1,

TMP.ADDRESSLINE2,

TMP.CITY,

TMP.STATE,

TMP.POSTALCODE,

TMP.COUNTRY,

TMP.TERRITORY,

TMP.INSERT_DTS,

TMP.UPDATE_DTS

);`});

 

//Create statement COMMIT, Commits an open transaction in the current session

 

snowflake.execute({sqlText:`COMMIT;`});

 

//Statement returned for info and debuging purposes

 

return "Store Procedure Executed Successfully";

}

 

catch (err)

{

    result = 'Error: ' + err;

    snowflake.execute({sqlText:`ROLLBACK;`});

    throw result;

}

$$;

CREATE OR REPLACE TASK SCD1_DB.PUBLIC.CUSTOMER_TASK

WAREHOUSE = COMPUTE_WH

SCHEDULE = '1 MINUTE'

WHEN SYSTEM$STREAM_HAS_DATA('SCD1_DB.PUBLIC.CUSTOMER_STREAM')

AS CALL SCD1_DB.PUBLIC.CUSTOMER_SP();

ALTER TASK SCD1_DB.PUBLIC.CUSTOMER_TASK RESUME;

SHOW TASKS;

Glue job sample data load python script

 import boto3

import requests

import base64

from botocore.exceptions import NoCredentialsError, PartialCredentialsError

# Example usage

github_url = "https://raw.githubusercontent.com/deacademygit/project-data/refs/heads/main/country_details.json"  # Raw file URL from GitHub

bucket_name = "sampleS3bucketname"  # The S3 bucket where the file will be uploaded

s3_key = "data/country_details.json"  # The key (path) in the S3 bucket

def fetch_data_from_github_and_upload_to_s3(github_url, bucket_name, s3_key):

    try:

        # Fetch the file content from GitHub (raw URL or GitHub API URL)

        response = requests.get(github_url)

        # Check if the request was successful

        if response.status_code == 200:

            # Get the content from the response (for raw URL, it's already plain text or binary)

            file_content = response.content

            # Initialize the S3 client

            s3_client = boto3.client('s3')

            # Upload the file content to S3

            s3_client.put_object(Bucket=bucket_name, Key=s3_key, Body=file_content)

            print(f"File uploaded successfully to s3://{bucket_name}/{s3_key}")

        else:

            print(f"Error: Failed to fetch file from GitHub. Status code {response.status_code}")

    except (NoCredentialsError, PartialCredentialsError) as e:

        print(f"Error: AWS credentials are missing or incomplete. {e}")

    except requests.exceptions.RequestException as e:

        print(f"Error fetching the file from GitHub: {e}")

    except Exception as e:

        print(f"Error uploading file to S3: {e}")

fetch_data_from_github_and_upload_to_s3(github_url, bucket_name, s3_key)

python script sample

 # Create a new DataFrame that includes only the title_name and genres columns, and remove the square brackets and spaces from the genres column

df = dim_titles_roku_python[['title_name', 'genres']].copy()  # make a copy to avoid modifying the original DataFrame

df.loc[:, 'genres'] = df['genres'].str.replace('[', '', regex=True).str.replace(']', '', regex=True).str.replace(' ', '', regex=True)

# df['genres'] = df['genres'].str.replace('[', '').str.replace(']', '').str.replace(' ', '')

# Use the `apply` function to split the genres column into a list of individual genres

df = df.apply(

    lambda x: pd.Series(x['genres'].split(',')),

    axis=1).stack().reset_index(level=1, drop=True)

# Use the `reset_index` function to create a new DataFrame where each genre is a separate row

df = df.reset_index(name='genre')

# Use the `groupby` and `size` functions to count the number of occurrences of each genre

df = df.groupby('genre').size().reset_index(name='frequency')

# Sort the resulting DataFrame by the frequency column in descending order

df = df.sort_values(by='frequency', ascending=False).head(10)

print(df)

Top & Bottom Categories each Year for Men and Woman based on Product Reviews

 df = dim_category_nike_python.copy()

#print(df)

product = dim_product_nike_python.copy()

product = product.merge(df,on='category_id',how='inner')

product = product[['order_date','gender','category_name','product_reviews']]

product['order_date'] = pd.to_datetime(product['order_date']).dt.year

product=product.groupby(['order_date','gender','category_name'])['product_reviews'].mean().reset_index()

product['top_rank']=product.groupby(['order_date','gender'])['product_reviews'].rank(ascending=False)

product['bottom_rank']=product.groupby(['order_date','gender'])['product_reviews'].rank(ascending=True)

new_df = product[(product['top_rank']==1)|(product['bottom_rank']==1)]

new_df = new_df.copy()

new_df.loc[:,'Category_type'] = new_df.apply(lambda row: 'Top' if row['top_rank']== 1 else 'Bottom',axis=1 )

grouped  =new_df.groupby(['order_date','gender','Category_type'])['category_name'].first().reset_index()

pivot_df = grouped.pivot(index='order_date',columns=['gender','Category_type'],values='category_name')

pivot_df.columns = [  f"{gender}_{abc}_Category"  for gender,abc in pivot_df.columns ]

pivot_df = pivot_df.reset_index()

column = {'men_Bottom_Category':'Men_Bottom_Category',

    'woman_Top_Category':'Woman_Top_Category',

    'woman_Bottom_Category':'Woman_Bottom_Category',

    'men_Top_Category':'Men_Top_Category',

    'order_date':'order_year'

    

}

pivot_df=pivot_df.rename(columns = column)

#print(product[product['order_date']==2020])

print(pivot_df)

python script to aggregate , merge, sort etc

 df= fact_orders_grubhub_python.merge(dim_delivery_person_grubhub_python, on='Delivery_person_ID')

# Use the `groupby` and `agg` functions to compute the average age and rating for each city_type

df = df.groupby('city_type').agg({

    'Delivery_person_Age': 'mean',

    'Delivery_person_Ratings': 'mean'

}).reset_index()

# Use the `round` function to round the age and rating columns to 2 decimal places

df['Delivery_person_Age'] = df['Delivery_person_Age'].round(2)

df['Delivery_person_Ratings'] = df['Delivery_person_Ratings'].round(2)

# Rename the columns to match the names in the SQL query

df.rename(columns={

    'Delivery_person_Age': 'avg_age',

    'Delivery_person_Ratings': 'avg_rating'

}, inplace=True)

print(df.head(5))