🚀 Data Processing with GCP, Apache Beam & Pandas 💡

I recently worked on a project where I processed and transformed data using Google Cloud Platform (GCP), Apache Beam, and Pandas, running everything inside a GCP notebook. This experience reinforced the power of serverless data processing and distributed computing for handling large-scale datasets.

🔹 Project Overview I used Apache Beam to process a CSV file stored in Google Cloud Storage (GCS). The pipeline reads the file, applies transformations, and stores the results in a BigQuery table. The whole process runs efficiently and scales seamlessly.

🔹 Tech Stack ✅ Google Cloud Storage (GCS) – Storing the input CSV file

✅ Apache Beam – Data processing and transformation

✅ Pandas – Data manipulation

✅ BigQuery – Storing the final dataset

🔹 How I Built It

1️⃣ Read the CSV file from GCS using Apache Beam

2️⃣ Transform the data – split lines, map values, and group by keys

3️⃣ Process and structure the data using Pandas

4️⃣ Write the results to a BigQuery table

Here’s a simplified version of the Apache Beam pipeline I used: