Getting Started with GCP

Overview and Introduction

Welcome! Today, we're venturing into the realms of cloud computing, Google Cloud Platform (GCP), and Python . Think of cloud computing as a vast library from which you can borrow, use, and return resources. We'll explore how to manage GCP services using the official Python client libraries. Important Note About This Course : Throughout this course, our practical exercises will run against local emulators rather than live GCP services. This approach allows you to learn and experiment safely without incurring costs or requiring actual GCP credentials. We'll explain how this works and how it differs from production environments. Additionally, in the CodeSignal environment, all necessary packages and emulators are already set up for you. Let's delve into the GCP ecosystem, examine the Python client libraries, understand both emulator and production authentication, and learn how to initialize and configure these libraries for your projects.

Introduction to Google Cloud Client Libraries for Python

The Google Cloud Client Libraries for Python equip developers with tools to integrate and manage GCP services within their applications. These libraries, such as google-cloud-firestore for Firestore or google-cloud-compute for Compute Engine, offer a convenient way to communicate with the wide array of GCP services. By using these libraries, you can automate tasks like managing documents in Firestore, managing virtual machines, or interacting with BigQuery datasets. Here's how you can include a Google Cloud client library in your Python code: Python from google.cloud import firestore By importing the relevant client library, you're creating a bridge between your application and the resources in your GCP project. These libraries allow your Python scripts to perform operations such as creating new resources, querying existing ones, and configuring services, all within the context of your specific GCP environment. The client libraries operate with the credentials you provide, which means they only have access to the GCP resources associated with your project and permissions.

Installing the Google Cloud SDK and Python Client Libraries

For personal projects outside of this course environment, you'll need to install the necessary tools and packages:

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Shell

gcloud auth login

Shell

# For Firestore
pip install google-cloud-firestore

# For Cloud Storage
pip install google-cloud-storage

# For BigQuery
pip install google-cloud-bigquery

# Or install multiple at once
pip install google-cloud-firestore google-cloud-storage google-cloud-bigquery

# For Firestore
pip install google-cloud-firestore

# For Cloud Storage
pip install google-cloud-storage

# For BigQuery
pip install google-cloud-bigquery

# Or install multiple at once
pip install google-cloud-firestore google-cloud-storage google-cloud-bigquery

Shell

pip install google-cloud

pip install google-cloud

Python

from google.cloud import firestore

# The FIRESTORE_EMULATOR_HOST environment variable is pre-configured
# in our course environment, so the client automatically connects to the emulator
client = firestore.Client()

# Reference a collection
collection = client.collection('users')

# Add a document to the collection
doc_ref = collection.add({
    'name': 'John Doe',
    'email': 'john.doe@example.com',
    'created_at': firestore.SERVER_TIMESTAMP
})

from google.cloud import firestore

# The FIRESTORE_EMULATOR_HOST environment variable is pre-configured
# in our course environment, so the client automatically connects to the emulator
client = firestore.Client()

# Reference a collection
collection = client.collection('users')

# Add a document to the collection
doc_ref = collection.add({
    'name': 'John Doe',
    'email': 'john.doe@example.com',
    'created_at': firestore.SERVER_TIMESTAMP
})

Python

from google.cloud import firestore

# For production, set the GOOGLE_APPLICATION_CREDENTIALS environment variable
# to the path of your service account key file before running your script:
# export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-file.json"

# Initialize the Firestore client (will use ADC for authentication)
client = firestore.Client()

# Reference a collection
collection = client.collection('users')

# Add a document to the collection
doc_ref = collection.add({
    'name': 'John Doe',
    'email': 'john.doe@example.com',
    'created_at': firestore.SERVER_TIMESTAMP
})

from google.cloud import firestore

# For production, set the GOOGLE_APPLICATION_CREDENTIALS environment variable
# to the path of your service account key file before running your script:
# export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-file.json"

# Initialize the Firestore client (will use ADC for authentication)
client = firestore.Client()

# Reference a collection
collection = client.collection('users')

# Add a document to the collection
doc_ref = collection.add({
    'name': 'John Doe',
    'email': 'john.doe@example.com',
    'created_at': firestore.SERVER_TIMESTAMP
})

Shell

gcloud auth login

Shell

# For Firestore
pip install google-cloud-firestore

# For Cloud Storage
pip install google-cloud-storage

# For BigQuery
pip install google-cloud-bigquery

# Or install multiple at once
pip install google-cloud-firestore google-cloud-storage google-cloud-bigquery

Shell

pip install google-cloud

Python

from google.cloud import firestore

# The FIRESTORE_EMULATOR_HOST environment variable is pre-configured
# in our course environment, so the client automatically connects to the emulator
client = firestore.Client()

# Reference a collection
collection = client.collection('users')

# Add a document to the collection
doc_ref = collection.add({
    'name': 'John Doe',
    'email': 'john.doe@example.com',
    'created_at': firestore.SERVER_TIMESTAMP
})

Python

from google.cloud import firestore

# For production, set the GOOGLE_APPLICATION_CREDENTIALS environment variable
# to the path of your service account key file before running your script:
# export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-file.json"

# Initialize the Firestore client (will use ADC for authentication)
client = firestore.Client()

# Reference a collection
collection = client.collection('users')

# Add a document to the collection
doc_ref = collection.add({
    'name': 'John Doe',
    'email': 'john.doe@example.com',
    'created_at': firestore.SERVER_TIMESTAMP
})

Overview and Introduction

Introduction to Google Cloud Client Libraries for Python

Installing the Google Cloud SDK and Python Client Libraries

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Installing the Google Cloud SDK

Installing Python Client Libraries

Understanding Local Emulators vs. Production GCP Services

Local Emulators (Used in This Course)

Production GCP Services

Understanding GCP Authentication for Production Environments

Initializing and Configuring GCP Client Libraries in Python

For Emulators (Our Course Environment)

For Production GCP Services

Lesson Summary and Upcoming Practice