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Get quick hands-on experience with Google Cloud. This cookbook provides a variety of self-contained recipes that show you how to use Google Cloud services for your enterprise application. Whether you're looking for practical ways to apply microservices, AI, analytics, security, or networking solutions, these recipes take you step-by-step through the process and provide discussions that explain how and why the recipes work.

Ideal for system engineers and administrators, developers, network and database administrators, and data analysts, this cookbook helps you get started with Google Cloud regardless of your level of experience. Google veterans Rui Costa and Drew Hodun also cover advanced-level Google Cloud services for those who have appreciable experience with the platform.

  • Learn how to get started with Google Cloud
  • Understand the depth of services Google Cloud provides
  • Gain hands-on experience using practical examples and labs
  • Explore topics that include BigQuery, Cloud Run, and Kubernetes
  • Build and run mobile and web applications on Google Cloud
  • Examine ways to build your cloud applications for scale
  • Build a minimum viable product (MVP) app to use in production
  • Learn data platform and pipeline skills

Table of Contents

  1. 1. Cloud Functions
    1. 1.1. Creating a public HTTP Google Cloud Function
    2. 1.2. Creating a Secure HTTP Google Cloud Function
    3. 1.3. Accessing Environment Variables at runtime
    4. 1.4. Sending Emails from Cloud Functions with SendGrid
    5. 1.5. Deploying Cloud Functions with a GitLab CI/CD Pipeline
    6. 1.6. Responding to SMS Messages with Twilio and Cloud Functions
    7. 1.7. Unit Testing with GitLab and Cloud Functions
    8. 1.8. Building an API Gateway to Gather Telemetry Data
  2. 2. Google Cloud Run
    1. 2.1. Deploying a Prebuilt Hello World Container
    2. 2.2. Building your own Hello World Container
    3. 2.3. Using Cloud Run with a custom domain
    4. 2.4. Triggering a Cloud Run from Cloud Pub/Sub
    5. 2.5. Deploy a Web application to Cloud Run
    6. 2.6. Rolling Back a Cloud Run Service Deployment
    7. 2.7. Cloud Run Gradual Rollouts
    8. 2.8. Cloud Run Configuration Parameters
  3. 3. Google App Engine
    1. 3.1. Deploy a Hello World to App Engine Standard
    2. 3.2. Deploy a Hello World to App Engine Flexible
    3. 3.3. Securing your application with Identity-Aware Proxy (IAP)
    4. 3.4. Custom Domains with App Engine
    5. 3.5. Using Machine Learning APIs with App Engine
    6. 3.6. Cube.js and React Dashboards with App Engine
    7. 3.7. Debugging an Instance
    8. 3.8. GitLab CI/CD and App Engine
  4. 4. Google Cloud Compute Engine
    1. 4.1. Create a Windows Virtual Machine
    2. 4.2. Create a Linux Virtual Machine and install NGNIX
    3. 4.3. Connecting to your Windows Virtual Machines with Identity-Aware Proxy TCP Forwarding
    4. 4.4. Virtual Machine OS Login with 2 Step Verification
    5. 4.5. Running Startup Scripts
    6. 4.6. Create a Cluster of NGINX web server with an Instance Group
    7. 4.7. Deploy Containers to Managed Instance Groups
    8. 4.8. Transferring Files to your Virtual Machine
    9. 4.9. Using VM Manager for Patch Management
    10. 4.10. Backuping your Virtual Machine
  5. 5. Google Cloud Kubernetes Engine
    1. 5.1. Create a Zonal Cluster
    2. 5.2. Create a Regional Cluster
    3. 5.3. Deploy a MongoDB Database with StatefulSets
    4. 5.4. Resizing a Cluster
    5. 5.5. Load Testing with Locust
    6. 5.6. Multi-Cluster Ingress
    7. 5.7. Continuous Delivery with Spinnaker and Kubernetes
    8. 5.8. Deploy a Spring Boot Java Application
    9. 5.9. Skaffold
    10. 5.10. GKE Autopilot
  6. 6. Working with Data
    1. 6.1. Speeding up GCE Transfers - Multiprocessing
    2. 6.2. Speeding up GCS Transfers - Parallel Composite Uploads for large files
    3. 6.3. Adding event timestamps to PubSub
    4. 6.4. Mounting GCS as a File-System (sort of)
    5. 6.5. Designing your Schema for Spanner with Interleaved Tables
    6. 6.6. Automatically archiving and deleting objects on GCS
    7. 6.7. Locking down Firestore Database so a User can edit only their data
  7. 7. BigQuery and Data Warehousing
    1. 7.1. Building a Pivot Table in BigQuery
    2. 7.2. Adding Partitioned and Clustered columns to an existing Table
    3. 7.3. Selecting the Top-1 Result (scalably)
    4. 7.4. De-duplicating Rows in BigQuery with Row Key
    5. 7.5. De-duplicating Rows in BigQuery with Timestamp
    6. 7.6. Un-deleting a Table in BigQuery
    7. 7.7. Streaming JSON or Avro Data into BigQuery with a Dataflow Template
  8. 8. Data Processing Tools
    1. 8.1. Triggering a Dataflow job Automatically from a GCS Upload
  9. 9. AI/ML
    1. 9.1. Creating an AI Platform Notebook
    2. 9.2. Training a Python Model Serverlessly
    3. 9.3. Serving a Python Model with Serverless
    4. 9.4. Get Explanations with your ML Predictions
    5. 9.5. Create a Custom Notebook Environment
    6. 9.6. Tensorflow Batch Predictions on Cloud AI Platform
    7. 9.7. BQ Data in Tensorflow or Pytorch Model
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