Python For GCP is a project aimed at simplifying the interaction with Google Cloud Platform (GCP) services using Python. This repository provides code examples and scripts that help you manage and automate various GCP resources such as BigQuery, Cloud Storage, BigTable, Compute Engine, and more entirely through Python.
- GCP Infrastructure Management with Python
Manage and provision GCP infrastructure components such as Virtual Machines (VMs), disks, and machine images using Python scripts. This enables Infrastructure as Code (IaC) workflows where you can automate environment setup, scale resources, and manage your infrastructure lifecycle without using the console or manual configuration.
Key capabilities:
Create and delete Compute Engine instances (VMs)
Attach or detach persistent disks to VMs
Create machine images from running instances
Restart VMs programmatically across a project or zone
- BigQuery Integration
Perform BigQuery operations using Python, such as loading datasets, querying tables, and exporting results. This is ideal for automating data pipelines, analytics, and reporting processes.
Key capabilities:
Load data into BigQuery tables from CSV/JSON files or Cloud Storage
Run SQL queries and retrieve results as Pandas DataFrames
Export data from BigQuery to Google Cloud Storage
Manage datasets and tables programmatically
- BigTable Integration
Ingest and retrieve time-series or NoSQL-style data using BigTable with Python. This is particularly useful for high-throughput, low-latency workloads.
Key capabilities:
Connect to Cloud BigTable instances and tables
Insert bulk rows and column families using Python clients
Read or scan data with filtering capabilities
- VM Automation
Automate routine VM operations across projects or instances using Python. This is valuable for saving costs, maintaining environments, or scheduling tasks.
Key capabilities:
Restart all VMs in a project or specific zone
Stop or delete VMs programmatically
List or manage VMs and their metadata
- Cloud Storage (GCS) Operations
Interact with Google Cloud Storage buckets and objects using Python, useful for handling unstructured data like media files, logs, backups, or datasets.
Key capabilities:
Create and delete GCS buckets
Upload and download objects/files
List objects within a bucket
Manage access permissions and object metadata
- Data Loss Prevention (DLP) Jobs
Use Python to perform sensitive data inspections across datasets or storage using Google Cloud DLP. This helps ensure compliance and data privacy.
Key capabilities:
Set up and execute DLP inspection jobs via API
Scan for sensitive information like PII, PHI, or financial data
Automate reports or trigger alerts based on findings