Google Cloud Cookbook

Table of contents

1. Preface
   1. Who Should Read This Book
   2. Why I Wrote This Book
   3. Navigating This Book
   4. Conventions Used in This Book
   5. Using Code Examples
   6. O’Reilly Online Learning
   7. How to Contact Us
   8. Acknowledgments
2. 1. Introduction
3. 2. Cloud Functions
   1. 2.1. Creating a Public HTTP Google Cloud Function
   2. 2.2. Authenticating an HTTP Google Cloud Function
   3. 2.3. Accessing Environment Variables at Runtime
   4. 2.4. Sending Emails from Cloud Functions with SendGrid
   5. 2.5. Deploying Cloud Functions with a GitLab CI/CD Pipeline
   6. 2.6. Responding to SMS Messages with Twilio and Cloud Functions
   7. 2.7. Unit Testing with GitLab and Cloud Functions
   8. 2.8. Building an API Gateway to Gather Telemetry Data
4. 3. Google Cloud Run
   1. 3.1. Deploying a Prebuilt Hello World Container
   2. 3.2. Building Your Own Hello World Container
   3. 3.3. Using Cloud Run with a Custom Domain
   4. 3.4. Triggering a Cloud Run from Cloud Pub/Sub
   5. 3.5. Deploying a Web Application to Cloud Run
   6. 3.6. Rolling Back a Cloud Run Service Deployment
   7. 3.7. Deploying Cloud Run Services in a Gradual Rollout
   8. 3.8. Cloud Run Configuration Parameters
5. 4. Google App Engine
   1. 4.1. Deploying a Hello World to App Engine (Standard)
   2. 4.2. Deploying a Hello World to App Engine (Flexible)
   3. 4.3. Securing Your Application with Identity-Aware Proxy
   4. 4.4. Mapping Custom Domains with App Engine
   5. 4.5. Using the Google Cloud Translation Machine Learning APIs with App Engine
   6. 4.6. Building User Interfaces for Viewing Charts and Graphs
   7. 4.7. Debugging an Instance
   8. 4.8. Using CI/CD
6. 5. Google Cloud Compute Engine
   1. 5.1. Creating a Windows Virtual Machine
   2. 5.2. Creating a Linux Virtual Machine and Installing NGINX
   3. 5.3. Connecting to Your Windows Virtual Machines with Identity-Aware Proxy TCP Forwarding
   4. 5.4. Securing Your Virtual Machine Logins with Two-Step Verification
   5. 5.5. Running Startup Scripts
   6. 5.6. Creating a Group of NGINX Web Servers with a Managed Instance Group
   7. 5.7. Deploying Containers to Managed Instance Groups
   8. 5.8. Transferring Files to Your Virtual Machine
   9. 5.9. Using VM Manager for Patch Management
   10. 5.10. Backing Up Your Virtual Machine
7. 6. Google Cloud Kubernetes Engine
   1. 6.1. Creating a Zonal Cluster
   2. 6.2. Creating a Regional Cluster
   3. 6.3. Resizing a Cluster
   4. 6.4. Automatically Routing Traffic to the Nearest Cluster with Multi-Cluster Ingress
   5. 6.5. Deploying a Spring Boot Java Application
   6. 6.6. Deploying a Java Application to Kubernetes, Using Skaffold
   7. 6.7. Using GKE Autopilot for Running an Application You Don’t Have to Manage
8. 7. Working with Data
   1. 7.1. Speeding Up Cloud Storage Bulk Transfers by Multiprocessing
   2. 7.2. Speeding Up GCS Transfers for Large Files with Parallel Composite Uploads
   3. 7.3. Mounting GCS as a Filesystem
   4. 7.4. Automatically Archiving and Deleting GCS Objects
   5. 7.5. Creating and Restoring from Persistent Disk Snapshots in GCE
   6. 7.6. Using Interleaved Tables in Your Cloud Spanner Database
   7. 7.7. Locking Down Firestore Database So a User Can Edit Only Their Data
9. 8. BigQuery and Data Warehousing
   1. 8.1. Using Cloud Console to Run a BigQuery Query
   2. 8.2. Loading Data to BigQuery from CSV
   3. 8.3. Building a Pivot Table in BigQuery
   4. 8.4. Adding Partitioned and Clustered Columns to an Existing Table
   5. 8.5. Adding Clustering to a Table That Can’t or Shouldn’t Be Partitioned
   6. 8.6. Selecting the Top-1 Result
   7. 8.7. Merging Tables in BigQuery Without Duplicates
   8. 8.8. Deduplicating Rows in BigQuery with Timestamps
   9. 8.9. Undeleting a Table in BigQuery
   10. 8.10. Streaming JSON or Avro Data into BigQuery with a Dataflow Template
10. 9. Data Processing Tools
    1. 9.1. Cleaning Data Using the Data Fusion GUI
    2. 9.2. Running a Simple Python Dataflow Pipeline
    3. 9.3. Building a Streaming Pipeline in Dataflow SQL
    4. 9.4. Querying BigQuery from a Dataproc Job
    5. 9.5. Adding Event Timestamps to Pub/Sub
    6. 9.6. Inferring and Using Schemas in Dataflow
    7. 9.7. Mini-batching and Streaming Dataflow Data to BigQuery Using Filters
    8. 9.8. Triggering a Dataflow Job Automatically from a GCS Upload
11. 10. AI/ML
    1. 10.1. Creating a Vertex AI Notebook
    2. 10.2. Training a Python ML Model Serverlessly
    3. 10.3. Making Serverless Predictions with a Python Model
    4. 10.4. Creating a Custom Notebook Environment
    5. 10.5. Extracting Data from BigQuery to Pandas for Model Training
    6. 10.6. Training a Model in SQL with BQML
12. 11. Google Cloud Security and Access
    1. 11.1. Creating a Service Account
    2. 11.2. Creating Custom Roles to Access a Cloud Storage Bucket
    3. 11.3. Authenticating an Application Running on Kubernetes Engine
    4. 11.4. Retrieving the Authenticated User’s Identity
    5. 11.5. Authenticating a Java Application Using a Service Account
    6. 11.6. Building Reports Using the Cloud Asset API
    7. 11.7. Allowing a List of IP Addresses to Access Your Application
13. 12. Google Cloud Networking
    1. 12.1. Creating a Custom Mode VPC Network
    2. 12.2. Creating a Static External IP Address
    3. 12.3. Create a Firewall Rule
    4. 12.4. Serving Content for Users in a Specific Region
    5. 12.5. Configuring VPC Network Peering
    6. 12.6. Creating VPN Gateways with Cloud Routers
    7. 12.7. Deployments of Networks Using Terraform
    8. 12.8. Limiting Access to Only Authorized Networks with VPC Service Controls
14. Index