Skip to content

Latest commit

 

History

History
141 lines (104 loc) · 3.81 KB

clcpulyvs000l08mk6va63vtd.md

File metadata and controls

141 lines (104 loc) · 3.81 KB
Raw

Covid cases fetching with GCP

Introduction

This blog is all about a pipeline that fetches daily covid data as it is orchestrated by apache-airflow and saves the data into the BigQuery table.

Pre-requisites

You should have a GCP account with admin or privileged access, and apache-airflow installed or you can use cloud composers also(GCP).

Procedures

This project consists of two files named as function.py which contains a couple of functions to fetch and save the data, dag.py which contains codes for airflow. I will also give a Dockerfile , in case you need this project to deploy.

  1. codes for function.py
# function to fetch the case data
def fetch_cases():
    # request to the API to get the case data
    response = requests.get('https://api.covid19api.com/dayone/country/india/status/confirmed')
    data = response.json()

    # Extract the relevant fields from the data
    cases = []
    for record in data:
        date = record['Date']
        cases = record['Cases']
        cases.append({'date': date, 'cases': cases})

    return cases


# Define a function to save the case data to BigQuery
def save_cases(**kwargs):
    # Extract the case data from the kwargs
    cases = kwargs['ti'].xcom_pull(task_ids='fetch_cases')

    # Create a BigQuery client
    client = bigquery.Client()

    # Create a new dataset
    dataset = bigquery.Dataset(client.dataset('covid_data_dataset'))
    dataset.location = 'US'
    dataset = client.create_dataset(dataset)

    # Create a new table in the dataset
    table = bigquery.Table(dataset.table('case_data_table'), schema=[
        bigquery.SchemaField('date', 'DATE'),
        bigquery.SchemaField('cases', 'INTEGER')
    ])
    table = client.create_table(table)

    # Load the data into the table
    errors = client.insert_rows(table, rows=cases)
    if errors:
        print(errors)
  1. codes for dag.py
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.utils.dates import days_ago
import requests
from datetime import timedelta
import json
from google.cloud import bigquery
from function import fetch_cases, save_cases

default_args = {
    'owner': 'my-covid-dag',
    'start_date': days_ago(2),
    'depends_on_past': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

dag = DAG(
    'covid_cases_fetch_push',
    default_args=default_args,
    description='Fetch daily COVID-19 case data and store it in BigQuery',
    schedule_interval='@daily',
)

fetch_cases_task = PythonOperator(
    task_id='fetch_cases',
    python_callable=fetch_cases,
    dag=dag,
)

save_cases_task = PythonOperator(
    task_id='save_cases',
    python_callable=save_cases,
    provide_context=True,
    dag=dag,
)


fetch_cases_task >> save_cases_task
  1. codes for Dckerfile
FROM python:3.10

RUN pip install requests
RUN pip install pip install python-csv

# Copy the code into the image
COPY covid_fetch.py .  # . means current directory, set yours.

# Set the working directory
WORKDIR .     # . means current directory, set yours.

CMD ["python", "covid_fetch.py"]

Save to local

If you don't want to save the data into tables instead you want the data into .csv format saved directly into your local directory then just modify function.py by changing save_cases function with the below codes.

# Define a function to save the case data to a CSV file
def save_cases(**kwargs):
    # Extract the case data from the kwargs
    cases = kwargs['ti'].xcom_pull(task_ids='fetch_cases')

    # Write the case data to a CSV file
    with open('./cases.csv', 'w', newline='') as csvfile:
        fieldnames = ['date', 'cases']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        for case in cases:
            writer.writerow(case)