Intelrunner

#!/bin/bash

# Set your GCP project ID
PROJECT_ID=$(gcloud config get-value project)

# List all functions in the project and iterate over them
for FUNCTION_NAME in $(gcloud functions list --project=$PROJECT_ID --format="value(name)")
do
    echo "Function: $FUNCTION_NAME"
    # Describe each function to get the dockerRegistry value

Intelrunner

"""
    This (Google Cloud) Function will scan all a list of provided projects and get all Bigquery datasets for each of those projects. 
    
    It will then update the storage billing model for each dataset to be 'PHYSICAL' instead of 'LOGICAL'. This is useful for large datasets that are frequently accessed, as it can reduce costs.
    
    Required Permissions:
    - The service account running this function must have the following roles:
     to be updated---
    Returns:
        string: A message indicating the function has completed.

Intelrunner

import functions_framework
from google.cloud import bigquery

def extract_dataset_name(full_path):
    """
    Extracts the dataset name from a full BigQuery dataset resource path.

    Args:
    - full_path (str): The full resource path of the dataset, 
                       formatted as 'projects/{project_id}/datasets/{dataset_id}'.

Intelrunner

steps:
  - name: golang
    entrypoint: go
    args: [ 'run', 'github.com/GoogleCloudPlatform/artifact-registry-go-tools/cmd/[email protected]', 'add-locations', '--locations=${_LOCATION}' ]
    env:
      # Set GOPROXY to the public proxy to pull the credential helper tool
      - 'GOPROXY=proxy.golang.org'

  - name: golang
    entrypoint: go

Intelrunner

#!/bin/bash

# prompt user for monorepo name
read -p "Enter monorepo name: " monorepo_name

# check if monorepo directory already exists
if [ -d "$monorepo_name" ]; then
  echo "Monorepo directory already exists. Skipping creation process."
else
  # create the main directory

Intelrunner

class GCPCostCalculator:
    def __init__(self):
        self.sql_cores = 0
        self.sql_ram = 0
        self.sql_disk_size = 0
        self.sql_disk_type = ""
        self.sql_num_machines = 0
        self.spanner_nodes = 0
        self.spanner_region = ""

Intelrunner

gcp_machine_family_pricing = {
    'N1': {
        'us-central1': {
            'core_cost': 0.031611,  # Cost per core-hour
            'ram_cost': 0.004237,  # Cost per GB-hour of RAM
            'cud_discount': {
                '1-year': 0.3,  # 30% discount
                '3-year': 0.57  # 57% discount
            }
        }

Intelrunner

#!/bin/bash
for project in $(gcloud projects list --format="value(projectId)")
do 
    for recommendation in $(gcloud recommender recommendations list --project=$project --location=global --recommender=google.compute.instance.MachineTypeRecommender)
    do
# Here is where the logic for your 'what to do with each recommendation' goes

        echo "$recommendation"
    done    
done

Intelrunner

WITH 
[CHURN STAT] AS 
(SELECT *
FROM [TABLE]
WHERE [STAT START] < '[DATE]'
AND (
  ([STAT END] >= '2018-01-01')
  OR ([STAT END]] IS NULL))
),
status AS 

Intelrunner

""" Warning - this script automatically submits a request for GCP DLP job creation
to scan for every table, in every dataset available to a user. This will fail if the following APIs / Permissions
are not enabled. 

This script can, and may, cost you actual $$. Outcomes can be seen in the DLP console.

DLP - jobs.create, jobs.get, jobs.list

BQ - bigquery.user (role)