Google Cloud Cookbook

Spis treści ebooka

• Preface
  • Who Should Read This Book
  • Why I Wrote This Book
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction
• 2. Cloud Functions
  • 2.1. Creating a Public HTTP Google Cloud Function
  • 2.2. Authenticating an HTTP Google Cloud Function
  • 2.3. Accessing Environment Variables at Runtime
  • 2.4. Sending Emails from Cloud Functions with SendGrid
  • 2.5. Deploying Cloud Functions with a GitLab CI/CD Pipeline
  • 2.6. Responding to SMS Messages with Twilio and Cloud Functions
  • 2.7. Unit Testing with GitLab and Cloud Functions
  • 2.8. Building an API Gateway to Gather Telemetry Data
• 3. Google Cloud Run
  • 3.1. Deploying a Prebuilt Hello World Container
  • 3.2. Building Your Own Hello World Container
  • 3.3. Using Cloud Run with a Custom Domain
  • 3.4. Triggering a Cloud Run from Cloud Pub/Sub
  • 3.5. Deploying a Web Application to Cloud Run
  • 3.6. Rolling Back a Cloud Run Service Deployment
  • 3.7. Deploying Cloud Run Services in a Gradual Rollout
  • 3.8. Cloud Run Configuration Parameters
• 4. Google App Engine
  • 4.1. Deploying a Hello World to App Engine (Standard)
  • 4.2. Deploying a Hello World to App Engine (Flexible)
  • 4.3. Securing Your Application with Identity-Aware Proxy
  • 4.4. Mapping Custom Domains with App Engine
  • 4.5. Using the Google Cloud Translation Machine Learning APIs with App Engine
  • 4.6. Building User Interfaces for Viewing Charts and Graphs
  • 4.7. Debugging an Instance
  • 4.8. Using CI/CD
• 5. Google Cloud Compute Engine
  • 5.1. Creating a Windows Virtual Machine
  • 5.2. Creating a Linux Virtual Machine and Installing NGINX
  • 5.3. Connecting to Your Windows Virtual Machines with Identity-Aware Proxy TCP Forwarding
  • 5.4. Securing Your Virtual Machine Logins with Two-Step Verification
  • 5.5. Running Startup Scripts
  • 5.6. Creating a Group of NGINX Web Servers with a Managed Instance Group
  • 5.7. Deploying Containers to Managed Instance Groups
  • 5.8. Transferring Files to Your Virtual Machine
  • 5.9. Using VM Manager for Patch Management
  • 5.10. Backing Up Your Virtual Machine
• 6. Google Cloud Kubernetes Engine
  • 6.1. Creating a Zonal Cluster
  • 6.2. Creating a Regional Cluster
  • 6.3. Resizing a Cluster
  • 6.4. Automatically Routing Traffic to the Nearest Cluster with Multi-Cluster Ingress
  • 6.5. Deploying a Spring Boot Java Application
  • 6.6. Deploying a Java Application to Kubernetes, Using Skaffold
  • 6.7. Using GKE Autopilot for Running an Application You Dont Have to Manage
• 7. Working with Data
  • 7.1. Speeding Up Cloud Storage Bulk Transfers by Multiprocessing
  • 7.2. Speeding Up GCS Transfers for Large Files with Parallel Composite Uploads
  • 7.3. Mounting GCS as a Filesystem
  • 7.4. Automatically Archiving and Deleting GCS Objects
  • 7.5. Creating and Restoring from Persistent Disk Snapshots in GCE
  • 7.6. Using Interleaved Tables in Your Cloud Spanner Database
  • 7.7. Locking Down Firestore Database So a User Can Edit Only Their Data
• 8. BigQuery and Data Warehousing
  • 8.1. Using Cloud Console to Run a BigQuery Query
  • 8.2. Loading Data to BigQuery from CSV
  • 8.3. Building a Pivot Table in BigQuery
  • 8.4. Adding Partitioned and Clustered Columns to an Existing Table
  • 8.5. Adding Clustering to a Table That Cant or Shouldnt Be Partitioned
  • 8.6. Selecting the Top-1 Result
  • 8.7. Merging Tables in BigQuery Without Duplicates
  • 8.8. Deduplicating Rows in BigQuery with Timestamps
  • 8.9. Undeleting a Table in BigQuery
  • 8.10. Streaming JSON or Avro Data into BigQuery with a Dataflow Template
• 9. Data Processing Tools
  • 9.1. Cleaning Data Using the Data Fusion GUI
  • 9.2. Running a Simple Python Dataflow Pipeline
  • 9.3. Building a Streaming Pipeline in Dataflow SQL
  • 9.4. Querying BigQuery from a Dataproc Job
  • 9.5. Adding Event Timestamps to Pub/Sub
  • 9.6. Inferring and Using Schemas in Dataflow
  • 9.7. Mini-batching and Streaming Dataflow Data to BigQuery Using Filters
  • 9.8. Triggering a Dataflow Job Automatically from a GCS Upload
• 10. AI/ML
  • 10.1. Creating a Vertex AI Notebook
  • 10.2. Training a Python ML Model Serverlessly
  • 10.3. Making Serverless Predictions with a Python Model
  • 10.4. Creating a Custom Notebook Environment
  • 10.5. Extracting Data from BigQuery to Pandas for Model Training
  • 10.6. Training a Model in SQL with BQML
• 11. Google Cloud Security and Access
  • 11.1. Creating a Service Account
  • 11.2. Creating Custom Roles to Access a Cloud Storage Bucket
  • 11.3. Authenticating an Application Running on Kubernetes Engine
  • 11.4. Retrieving the Authenticated Users Identity
  • 11.5. Authenticating a Java Application Using a Service Account
  • 11.6. Building Reports Using the Cloud Asset API
  • 11.7. Allowing a List of IP Addresses to Access Your Application
• 12. Google Cloud Networking
  • 12.1. Creating a Custom Mode VPC Network
  • 12.2. Creating a Static External IP Address
  • 12.3. Create a Firewall Rule
  • 12.4. Serving Content for Users in a Specific Region
  • 12.5. Configuring VPC Network Peering
  • 12.6. Creating VPN Gateways with Cloud Routers
  • 12.7. Deployments of Networks Using Terraform
  • 12.8. Limiting Access to Only Authorized Networks with VPC Service Controls
• Index

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