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