Introduction to GCP (Google Cloud Platform)

GCP is a powerful and rapidly growing cloud platform by Google, offering scalable infrastructure and advanced data analytics capabilities. For a Data Engineer, GCP provides essential services like Cloud Storage for data lakes, BigQuery for analytics, and Dataflow for building scalable data pipelines.

🔗 Learning Resources

• Official Training: Google Cloud Digital Leader Training
• Storage Deep-Dive: Cloud Storage Documentation
• Data Engineering Path: Google Cloud Skills Boost - Data Engineering
• BigQuery Guide: BigQuery Documentation

⚙️ Key GCP Services for Data Engineering

• Cloud Storage → Data lake storage for raw and processed data
• BigQuery → Serverless data warehouse for analytics
• Dataflow → Stream & batch data processing (Apache Beam)
• Pub/Sub → Real-time messaging and event ingestion
• Dataproc → Managed Spark/Hadoop clusters
• Cloud Composer → Workflow orchestration (Apache Airflow)

🧪 Practice Tasks

1. The Bucket Builder

Create a Cloud Storage bucket, upload a .csv file, and configure IAM permissions to allow read-only access.

2. The Cost Optimizer

Set up a lifecycle rule in Cloud Storage to move objects to Coldline or Archive storage after 30 days.

3. The Query Explorer

Load your .csv file into BigQuery and run SQL queries:

• Count total rows
• Filter records
• Aggregate data (e.g., GROUP BY)

4. The Access Controller

Create a service account with least privilege and use it to access BigQuery or Cloud Storage.

🚀 Mini Project: Build a Data Pipeline in GCP

Objective:

Build a simple batch data pipeline from Cloud Storage → BigQuery.

Steps:

1. Upload a sample dataset (.csv) to Cloud Storage
2. Create a BigQuery dataset and table
3. Use BigQuery UI / bq command / Dataflow template to load data
4. Transform data using SQL (e.g., cleaning, filtering)
5. Schedule pipeline (optional) using Cloud Composer or scheduled queries