Transfer/Stream Data/CSV Files from POS (Point of Sales) to GCS Buckets and then to BigQuery

Are you tired of manually uploading CSV files from your Point of Sale (POS) system to Google Cloud Storage (GCS) and then to BigQuery? Do you wish there was a way to automate this process and focus on more important things? Well, you’re in luck! In this article, we’ll show you how to transfer/stream data/CSV files from POS to GCS Buckets and then to BigQuery in a few easy steps.

Step 1: Set up Your POS System

Before we dive into the transfer process, make sure your POS system is set up to export data in a CSV format. This format is compatible with most POS systems, and it’s easy to work with in GCS and BigQuery. Consult your POS system’s documentation to learn how to set up CSV exports.

Common POS Systems and CSV Export Options

• Square: Go to Settings > Data & Analytics > Export data and select CSV as the file format.
• Shopify: Go to Settings > Analytics > Reports > Export data and select CSV as the file format.
• Upserve: Go to Settings > Reporting > Export data and select CSV as the file format.

Step 2: Create a GCS Bucket

Next, create a GCS Bucket to store your CSV files. If you already have a bucket, you can skip this step. Otherwise, follow these steps:

1. Go to the Google Cloud Console and navigate to the Navigation menu > Storage > Browser.
2. Click on Create bucket and enter a unique name for your bucket.
3. Select a Location for your bucket (e.g., US, EU, or Asia).
4. Choose the Storage class (e.g., Standard, Nearline, or Coldline) based on your storage needs.
5. Click Create to create your GCS Bucket.

Step 3: Set up a Cloud Function to Transfer CSV Files from POS to GCS

Now, we’ll create a Cloud Function to automatically transfer CSV files from your POS system to your GCS Bucket. This function will run on a schedule, so you can choose how often you want to transfer data.

Create a New Cloud Function

1. Go to the Google Cloud Console and navigate to the Navigation menu > Cloud Functions.
2. Click on Create function and enter a name for your function.
3. Select Cloud Scheduler as the trigger type.
4. Choose the Frequency of your schedule (e.g., every 15 minutes, hourly, or daily).
5. In the Runtime section, select Python 3.8 as the runtime environment.

Cloud Function Code

import requests
import csv
from google.cloud import storage

# Replace with your POS system's API URL and credentials
pos_api_url = 'https://your-pos-system.com/api/export'
pos_api_username = 'your-username'
pos_api_password = 'your-password'

# Replace with your GCS Bucket name
bucket_name = 'your-gcs-bucket-name'

def transfer_csv_files(event, context):
    # Authenticate with your POS system's API
    response = requests.get(pos_api_url, auth=(pos_api_username, pos_api_password))

    # Check if the response was successful
    if response.status_code == 200:
        # Parse the CSV data
        csv_data = response.text

        # Create a GCS client
        storage_client = storage.Client()

        # Get a reference to your GCS Bucket
        bucket = storage_client.get_bucket(bucket_name)

        # Create a new blob in your GCS Bucket
        blob = bucket.blob('sales_data.csv')

        # Upload the CSV data to the blob
        blob.upload_from_string(csv_data, content_type='text/csv')

        print(f'Uploaded CSV file to GCS Bucket: {bucket_name}')
    else:
        print(f'Failed to retrieve CSV data from POS system: {response.status_code}')

Step 4: Load Data from GCS to BigQuery

Now that your CSV files are stored in your GCS Bucket, we’ll create a BigQuery dataset and table to load the data.

Create a New BigQuery Dataset

1. Go to the Google Cloud Console and navigate to the Navigation menu > BigQuery.
2. Click on Create dataset and enter a name for your dataset.
3. Select the Data location (e.g., US, EU, or Asia) that matches your GCS Bucket location.
4. Click Create to create your BigQuery dataset.

Create a New BigQuery Table

1. Go to the Google Cloud Console and navigate to the Navigation menu > BigQuery.
2. Select your dataset and click on Create table.
3. Select Google Cloud Storage as the source.
4. Enter the GCS Bucket and file name (e.g., sales_data.csv) and click Create.

BigQuery Table Schema

Column name	Data type
date	DATE
product_id	INTEGER
product_name	STRING
quantity	INTEGER
price	FLOAT

In this example, we’ve created a table with five columns: date, product_id, product_name, quantity, and price. Adjust the schema to fit your specific needs.

Conclusion

That’s it! You’ve successfully set up a system to transfer/stream data/CSV files from your POS system to GCS Buckets and then to BigQuery. This automation will save you time and effort, allowing you to focus on more important tasks.

Remember to monitor your Cloud Function and BigQuery table to ensure that data is being transferred correctly. You can also modify the Cloud Function code to handle errors and exceptions.

Happy automating!

Frequently Asked Questions

Still got questions about transferring data from your Point of Sale (POS) to Google Cloud Storage (GCS) Buckets and then to BigQuery? Fear not, friend! We’ve got the answers you’re looking for.