Google Cloud Platform Expert

Expert guidance for Google Cloud Platform services and cloud-native architecture.

Core Concepts

• Compute Engine, App Engine, Cloud Run

• Cloud Functions (serverless)

• Cloud Storage

• BigQuery (data warehouse)

• Firestore (NoSQL database)

• Pub/Sub (messaging)

• Google Kubernetes Engine (GKE)

gcloud CLI

Initialize

gcloud init

Create Compute Engine instance

gcloud compute instances create my-instance
--zone=us-central1-a
--machine-type=e2-medium
--image-family=ubuntu-2004-lts
--image-project=ubuntu-os-cloud

Deploy App Engine

gcloud app deploy

Create Cloud Storage bucket

gsutil mb gs://my-bucket-name/

Upload file

gsutil cp myfile.txt gs://my-bucket-name/

Cloud Functions

import functions_framework from google.cloud import firestore

@functions_framework.http def hello_http(request): request_json = request.get_json(silent=True) name = request_json.get('name') if request_json else 'World'

return f'Hello {name}!'

@functions_framework.cloud_event def hello_pubsub(cloud_event): import base64 data = base64.b64decode(cloud_event.data["message"]["data"]).decode() print(f'Received: {data}')

BigQuery

from google.cloud import bigquery

client = bigquery.Client()

Query

query = """ SELECT name, COUNT(*) as count FROM project.dataset.table WHERE date >= '2024-01-01' GROUP BY name ORDER BY count DESC LIMIT 10 """

query_job = client.query(query) results = query_job.result()

for row in results: print(f"{row.name}: {row.count}")

Load data

dataset_id = 'my_dataset' table_id = 'my_table' table_ref = client.dataset(dataset_id).table(table_id)

job_config = bigquery.LoadJobConfig( source_format=bigquery.SourceFormat.CSV, skip_leading_rows=1, autodetect=True )

with open('data.csv', 'rb') as source_file: job = client.load_table_from_file(source_file, table_ref, job_config=job_config)

job.result()

Firestore

from google.cloud import firestore

db = firestore.Client()

Create document

doc_ref = db.collection('users').document('user1') doc_ref.set({ 'name': 'John Doe', 'email': 'john@example.com', 'age': 30 })

Query

users_ref = db.collection('users') query = users_ref.where('age', '>=', 18).limit(10)

for doc in query.stream(): print(f'{doc.id} => {doc.to_dict()}')

Real-time listener

def on_snapshot(doc_snapshot, changes, read_time): for doc in doc_snapshot: print(f'Received document: {doc.id}')

doc_ref.on_snapshot(on_snapshot)

Pub/Sub

from google.cloud import pubsub_v1

Publisher

publisher = pubsub_v1.PublisherClient() topic_path = publisher.topic_path('project-id', 'topic-name')

data = "Hello World".encode('utf-8') future = publisher.publish(topic_path, data) print(f'Published message ID: {future.result()}')

Subscriber

subscriber = pubsub_v1.SubscriberClient() subscription_path = subscriber.subscription_path('project-id', 'subscription-name')

def callback(message): print(f'Received: {message.data.decode("utf-8")}') message.ack()

streaming_pull_future = subscriber.subscribe(subscription_path, callback=callback)

Best Practices

• Use service accounts

• Implement IAM properly

• Use Cloud Storage lifecycle policies

• Monitor with Cloud Monitoring

• Use managed services

• Implement auto-scaling

• Optimize BigQuery costs

Anti-Patterns

❌ No IAM policies ❌ Storing credentials in code ❌ Ignoring costs ❌ Single region deployments ❌ No data backup ❌ Overly broad permissions

Resources

• GCP Documentation: https://cloud.google.com/docs

• gcloud CLI: https://cloud.google.com/sdk/gcloud