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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.\n\n# Feautures\n\n1. GCP Infrastructure Management with Python\n   \nManage 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.\n\nKey capabilities:\n\nCreate and delete Compute Engine instances (VMs)\n\nAttach or detach persistent disks to VMs\n\nCreate machine images from running instances\n\nRestart VMs programmatically across a project or zone\n\n\n2. BigQuery Integration\n   \nPerform BigQuery operations using Python, such as loading datasets, querying tables, and exporting results. This is ideal for automating data pipelines, analytics, and reporting processes.\n\nKey capabilities:\n\nLoad data into BigQuery tables from CSV/JSON files or Cloud Storage\n\nRun SQL queries and retrieve results as Pandas DataFrames\n\nExport data from BigQuery to Google Cloud Storage\n\nManage datasets and tables programmatically\n\n\n3. BigTable Integration\n   \nIngest and retrieve time-series or NoSQL-style data using BigTable with Python. This is particularly useful for high-throughput, low-latency workloads.\n\nKey capabilities:\n\nConnect to Cloud BigTable instances and tables\n\nInsert bulk rows and column families using Python clients\n\nRead or scan data with filtering capabilities\n\n\n4. VM Automation\n   \nAutomate routine VM operations across projects or instances using Python. This is valuable for saving costs, maintaining environments, or scheduling tasks.\n\nKey capabilities:\n\nRestart all VMs in a project or specific zone\n\nStop or delete VMs programmatically\n\nList or manage VMs and their metadata\n\n\n5. Cloud Storage (GCS) Operations\n   \nInteract with Google Cloud Storage buckets and objects using Python, useful for handling unstructured data like media files, logs, backups, or datasets.\n\nKey capabilities:\n\nCreate and delete GCS buckets\n\nUpload and download objects/files\n\nList objects within a bucket\n\nManage access permissions and object metadata\n\n6. Data Loss Prevention (DLP) Jobs\n   \nUse Python to perform sensitive data inspections across datasets or storage using Google Cloud DLP. This helps ensure compliance and data privacy.\n\nKey capabilities:\n\nSet up and execute DLP inspection jobs via API\n\nScan for sensitive information like PII, PHI, or financial data\n\nAutomate reports or trigger alerts based on findings\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsanjay-k08%2Fpython-for-gcp-interact-with-google-cloud-using-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsanjay-k08%2Fpython-for-gcp-interact-with-google-cloud-using-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsanjay-k08%2Fpython-for-gcp-interact-with-google-cloud-using-python/lists"}