前言

官网: https://cloud.google.com/learn/certification/cloud-digital-leader
网课：https://www.udemy.com/course/google-cloud-digital-leader-certification/
题库: https://www.udemy.com/course/latest-gcp-cdl-google-cloud-digital-leader-practice-exams-tests/
题库: https://www.examtopics.com/exams/google/cloud-digital-leader/view/

知识点

1. Regions and Zones

Regions and Zones

  • Google provide 20+ regions around the world
  • High Availability, Low Latency, Global Footprint, Government Regulation

  • Each Region has three or more Zones
  • Advantage of Zones: Increase availability and fault tolerance

2. Compute

Compute Engine

  • Compute Engine (GCE): Provision & Manage Virtual Machines

  • Create and manage lifecycle of VM instances
  • Load balancing and auto scaling for VM instances
  • Can attach storage, manage network connectivity and configuration

VM Setup

  • Startup Script: For boostrapping
    • Install OS patches or software when an VM instance is launched

  • Instance Templates: Specify VM instance details
    • Used to create VM instances and managed instance groups
    • Cannot be updated, need to be deleted and modified

  • Custom Image: Prefered way than Startup Script (推荐的做法)
    • Can have OS patches and software pre-installed

Use Discount

  • Sustained Use Discounts
    • Automatic discounts for running VM instances for significant portion of the billing month (意思就是在一个月内使用这个 instance 到一定程度就可以获得 discount)

  • Committed Use Discounts
    • 1 year or 3 year reservations for workloads with predictable resource needs

Preemptible VM

  • Preemptible VM: Short-lived cheaper (up to 80%) compute instances for non time-critical, fault-tolerant workloads (只能存在 24 小时)

Spot VM

  • Spot VM: Latest version of preemptible VM (这个就是 Spot Instance)
  • Does not have a maximum runtime, 没有 24 小时限制

Sole Tenant Nodes

  • Sole Tenant Nodes: 类似 Dedicated Host 或者 Dedicated Instance
  • Virtualized instances on hardware dedicated to one customer

Custom Machine Types

  • Custom Machine Types: 可以自定义 CPU, memory, GPU

VM costs

  • 2 primary costs in running VMs using GCE
  • Infrastructure cost (VM) & Licensing cost (OS)

Instance Groups

  • Instance Group: Group of VM instances managed as a single entity
  • Two Types of Instance Groups
    • Managed: Identical VMs created using a template
    • Unmanaged: Different configuration for VMs in same group
  • Location can be Zonal or Regional (Regional gives higher availability)

Managed Instance Groups

  • Managed Instance Groups: Maintain certain number of instances
    • Detect application failures using health check (self healing)
    • Increase and decrease instances based on load (auto scaling)
    • Add load balancer to distribute load

Cloud Load balancing

  • Cloud Load balancing: Distribute traffic across VM instances in one or more regions

3. Managed Services

IAAS (Infrastructure as a Service)

  • Use only infrastructure from cloud provider
    • Using VM to deploy your applications or databases

PAAS (Platform as a Service)

  • Use a platform provided by cloud
    • You are responsible for Configuration and Application code

SAAS (Software as a Service)

  • Centrally hosted software (mostly on the cloyd)
    • Offered on a subscription basis (pay-as-you-go)

Containers

  • Container ensure we have one way of deploying any microservices

  • Create Docker images for each microservice, include
    • Application Runtime
    • Application code and Dependencies
  • Runs the same way on any infrastructure
  • Advantage: light weight (No Guest OS), isolation for containers, cloud neutral

  • Container Orchestration (Many build upon Kubernetes)
    • Feature: Auto Scaling, Load Balancing, Self Healing, Fast Deployment

Serverless

  • Focus on code and the cloud managed service take cares of other stuffs
  • Pay for use

Shared Responsibility Model

  • Securiet in cloud is a Shared Responsibility
  • SaaS: Content + Access Policies + Usage
  • PaaS: SaaS + Deployment + Web Application Security
  • IaaS: PaaS + Operations + Network Security + Guest OS
  • Google Cloud is always responsible for Hardware, Network, Audit Logging etc

GCP Service Category

  • Compute Engine: IAAS (provide customize OS)
  • Google Kubernetes Engine: CAAS
  • App Engine: PAAS
  • Cloud Functions: FAAS
  • Cloud Run: CAAS (run one container quickly)

4. Managed Compute Service in GCP

App Engine

  • App Engine: Simplest way to deploy and scale your applications in GCP
    • Automatic load balancing & auto scaling
    • Managed platform update & health monitoring
    • Application versioning
    • Traffic splitting
  • No usage charges -> pay for resources provisioned

  • Compute Engine vs. App Engine
    • Compute Engine is IAAS, App Engine is PAAS
    • App Engine is serverless
    • Compute Engine you have more Responsibility than App Engine
    • App Engine is lower flexibility

App Engine Environments

  • Standard: Applications run in language specific sandboxes

    • Run in language specific sandbox
    • Supports scale down to Zero instances

  • Flexible: Application instances run within Docker containers

    • Support ANY runtime
    • CANNOT scale down to Zero instances

Google Kubernetes Engine (GKE)

  • Google Kubernetes Engine: Managed Kubernetes service
  • Minimize operations with auto-repair (repair failed nodes) and auto-upgrade (use latest version of K8S always) features
  • Provides Pod and Cluster Autoscaling
  • Two Modes: Standard (You manage) & Autopilot (GKE manage)

Cloud Functions (GCF)

  • Cloud Functions: Excute some code when an event happen
  • Don’t worry about servers or scaling or availability (only worry about your code)
  • Pay only for what you use
  • Time Bound - Default 1 min and MAX 60 minutes(3600 seconds)

Cloud Run & Anthos

  • Cloud Run: “Container to Production in Seconds”

    • Fully managed serverless platform for containerized applications

  • Cloud Run for Anthos: Deploy your workloads to Anthos clusters running on-premises or on Google Cloud

Compute Service in GCP

  • Create Virtual Machines: Compute Engine
  • Create a group of similar VMs: Managed Image Group
  • Distribute load among VMs: Cloud Load Balancing
  • Simplify setting up web application: App Engine
  • Easiest way to run one container: Google Cloud Run
  • Orchestrate containers: Google Kubernetes Engine
  • Build serverless event driven functions: Cloud Functions
  • Manage multi-cloud and on-premise Kubernetes clusters: Anthos

5. Storage

Block Storage & File Storage

Block Storage

  • Persistent Disk: Network Block Storage (Zonal or Regional)
  • Local SSDs: Local Block Storage

File Storage

  • Filestore: High performance file storage

Cloud Storage

  • Most popular, very flexible & inexpensive storage service
  • Store large objects using a key-value approach (Object Storage)
  • Provides REST API to access and modify objects
  • Store all file types - text, binary, backup & archives

Storage Classes

  • Different kinds of data can be stored in Cloud Storage
    • Can I pay a cheaper price for objects I access less frequently
  • Storage classes help to optimize your costs based on your access needs

  • Standard: Frequently used data/Short period of time
  • Nearline storage: Read or modify once a month on average (30 天内读写一次)
  • Coldline storage: Read or modify at most once a quarter (90 天内读写一次)
  • Archive storage: Less than once a year (365 天内读写一次)

Object Lifecycle Management

  • How do you save costs by moving files automatically between storage classes

    • Object Lifecycle Management

  • Identify objects using conditions based on

    • Age, CreatedBefore, IsLive, MatchesStorageClass, NumberOfNewerVersions
    • Set multiple conditions: all conditions must be satisfied for action to happen

  • Two kinds of actions

    • SetStorageClass actions (change from one storage class to another)
    • Deletion actions (delete objects)


Transferring data from On-Premises to Cloud

  • Most popular data destination is Google Cloud Storage
    • Online Transfer: Use gsutil or API to transfer data to Google Cloud Storage (data < 1 TB or from on-premise)
    • Storage Transfer Service: Recommended for large-scale (petabytes) online data transfers from your private data centers, AWS, Azure, and Google Cloud (data > 1 TB or from another cloud)
    • Transfer Appliance: Physical transfer using an appliance (data > 20 TB or time > 1 week)

Storage in GCP

  • A shared space for collaborating on media projects that involve large files: Filestore (File Storage)
  • A cost-effective solution to store and serve a large amount of unstructured data (Videos, Music, Files) globally: Cloud Storage (Object Storage)
  • Data is automatically managed and transitioned between storage classes to reduce costs: Object Lifecycle Management in Cloud Storage
  • A massive, one-time migration of data to the cloud, where online transfer is not feasible: Using Transfer Appliance for large- scale, physical data migration

6. Database Fundamentals

Cloud SQL & Cloud Spanner (OLTP)

  • Applications where large number of users make large number of small transactions

    • Popular databases: MySQL, Oracle, SQL Server etc

  • Recommended Google Managed Services

    • Cloud SQL: Supports PostgreSQL, MySQL, and SQL Server for regional relational databases
    • Cloud Spanner: Unlimited scale (multiple PBs) and 99.999% availability for global applications with horizontal scaling

BigQuery (OLAP)

  • Applications allowing users to analyze petabytes of data

    • Reporting applications, Data ware houses, Business intelligence applications, Analytics systems

  • Recommended Google Managed Services

    • BigQuery: Petabyte-scale distributed data ware house

OLAP vs OLTP

  • OLAP and OLTP use similar data structures, but different in how data is stored
  • OLTP databases use row storage
    • Efficient for processing small transactions
  • OLAP databases use columnar storage
    • High compression, Distribute data, Execute single query across multiple nodes

Cloud Firestore vs BigTable (NOSQL)

  • NoSQL databases trade-off “Strong consistency and SQL features” to achieve “scalability and high-performance”
  • NoSQL = not only SQL
  • Google Managed Services: Cloud Firestore (Datastore) & BigTable

  • Cloud Datastore - Managed serverless NoSQL document database

    • Designed for transactional mobile and web applications

  • BigTable - Managed, scalable NoSQL wide column database

    • Recommended for large analytical (> 10 TB) and operational workloads (not serverless)

Memory Store (In-memory Databases)

  • Retrieving data from memory is much faster than retrieving data from disk (Redis)
  • Recommended GCP Managed Service: Memory Store
  • Use cases: Caching, session management, gaming leader boards, geospatial applications

Database in GCP

Relational OLTP databases: Cloud SQL, Cloud Spanner

  • Have predefined schema and very strong transcational capabilities (Row storage)

Relational OLAP databases: BigQuery

  • Columnar storage with predefined schema. Datawarehouse & BigData workloads

NoSQL Databases: Cloud Firestore (Datastore), BigTable

  • Apps that need quickly evolving structure (schema-less)

In memory databases/caches: Memory Store

  • Applications needing microsecond responses

7. IAM

Cloud IAM

  • How do you identify users in GCP?
    • Identity and Access Management (Cloud IAM) provides this service

IAM Example

  • Provide access to manage a specific cloud storage bucket to a colleague
    • Choose a Role with right permissions (Ex: Storage Object Admin)
    • Create Policy binding member (your friend) with role (permissions)
  • Roles: A set of permissions (to perform specific actions on specific resources)

IAM Roles

  • Roles are Permissions: Perform some set of actions on some set of resources
    • Basic Roles - Owner / Editor / Viewer
    • Predefined Roles - Fine grained roles predefined and managed by Google
    • Custom Roles - When predefined roles are NOT sufficient, you can create your own custom roles

IAM Policy

  • Roles are assigned to users through IAM Policy documents
  • Represented by a policy object


8. Encryption

Data Lifecycle States

  • Data at rest: Stored on a device or a backup
  • Data in motion: Being transferred across a network
  • Data in use: Active data processed in a non-persistent state

Encryption

  • Symmetric Key Encryption
  • Use the same key for encryption and decryption

  • Asymmetric Key Encryption
  • Encrypt data with Public Key and decrypt with Private Key

Cloud KMS

  • Cloud KMS: Create and manage cryptographic keys (symmetric and asymmetric)

9. Organizing GCP Resources

Resource Hierarchy

  • Organization > Folder > Project > Resources
  • Resources are created in projects
  • A Folder can contain multiple projects
  • Organization can contain multiple Folders

Billing Accounts

  • Billing Account is mandatory for creating resources in a project
  • Setup a Cloud Billing Budget to avoid surprises - Alerts

IAM Best Practices

  • Principle of Least Privilege: Give least possible privilege needed for a role
  • Separation of Duties: Involve atleast 2 people in sensitive tasks
  • Constant Monitoring: Review Cloud Audit Logs to audit changes to IAM policies and access to Service Account keys

Public, Private, Hybrid Cloud

  • Public Cloud: You host everything in the cloud
    • DO NOT need a data center，NO Capital Expenditure needed
    • Hardware resources are owned by Google Cloud
  • Private Cloud: You host everything in your own data center
    • Needs Capital Expenditure

  • Hybrid Cloud: Combination of both (Public & Private)
    • Use Public Cloud for some workloads and Private cloud for others
  • Multi Cloud: Using Multiple Cloud Platforms with/without on￾premise infrastructure

Cloud VPN

  • Cloud VPN: Connect on-premise network to the GCP network
    • Encrypted
    • For low bandwidth, Cloud VPN is recommended

Cloud Interconnect

  • Cloud Interconnect: High speed physical connection between on-premise and VPC networks
    • Highly available with high throughput
    • Use only for high bandwidth needs

Organization Policy Service

  • How to enable centralized constraints on all resources created in an Organization?
    • Configure Organization Policy
  • Needs a Role - Organization Policy Administrator
  • IAM focuses on WHO, Organization Policy focuses on WHAT

Corporate Directory Federation

  • Federate Cloud Identity or Google Workspace with your external identity provider (IdP) such as Active Directory or Azure Active Directory

Identity Platform

  • Identity Platform: Customer identity and access management
  • Difference between Cloud IAM and Identity Platform
    • Cloud IAM: Employees and Partners Authorization
    • Identity Platform: Customer identity and access management

10. DevOps

CI, CD Tools

  • Cloud Source Repositories: Fully-featured, private Git repository
  • Container Registry: Store your Docker images
  • Cloud Build: Build deployable artifacts from your source code and configuration

Container Registry and Artifact Registry

  • Container Registry: Uses GCS bucket to store images, supports Container images only
  • Artifact Registry: Evolution of Container Registry, manage BOTH container images and non-container artifacts

Infrastructure as code

  • Treat infrastructure the same way as application code
  • Bring repeatability into your infrastructure
  • GCP service: Google Cloud Deployment Manager

Cloud Operations & Insights

  • Monitoring - Metrics and Alerts: Cloud Monitoring
  • Centralized Logging: Cloud Logging
  • Audit Logging: Cloud Audit Logs
  • Real-time exception monitoring: Error Reporting
  • Live Debugging: Cloud Debugger
  • Distributed tracing: Cloud Trace
  • Statistical, low-overhead profiler: Cloud Profiler

Site Reliability Engineering (SRE)

  • SRE teams focus on every aspect of an application
    • Manage by Service Level Objectives (SLOs)
    • Minimize Toil
    • Move Fast by Reducing Cost of Failure
    • Share Ownership with Developers

SRE - Key Metrics

  • Service Level Indicator(SLI): Quantitative measure of an aspect of a service
    • Categories: availability, latency, throughput, durability, correctness (error rate)
  • Service Level Objective (SLO) - SLI + target
    • 99.99% Availability, 99.999999999% Durability
  • Service Level Agreement (SLA): SLO + consequences (contract)
    • What is the consequence of NOT meeting an SLO? (Defined in a contract)
  • Error budgets: (100% – SLO)
    • How well is a team meeting their reliability objectives?

SRE - Best Practices

  • Handling Excess Loads: Load Shedding, Reduced Quality of Service
  • Avoiding Cascading Failures: Plan to avoid thrashing

  • Penetration Testing (Ethical Hacking)
  • Load Testing (JMeter, LoadRunner, Locust, Gatling etc)

  • Resilience Testing - “How does an application behaves under stress?”

11. Pub/Sub

  • Synchronous Communication: Applications makes synchronous calls to the logging service (What if the logging service goes down?)

  • Asynchronous Communication: Create a topic and have applications put log messages on the topic. Logging service picks them up for processing when ready

Pub/Sub

  • Pub/Sub: Reliable, scalable, fully-managed asynchronous messaging service
  • Backbone for Highly Available and Highly Scalable Solutions
  • Event ingestion and delivery for streaming analytics pipelines

  • Publisher - Sender of a message
  • Subscriber - Receiver of the message
    • Pull - Subscriber pulls messages when ready
    • Push - Messages are sent to subscribers

Cloud Dataflow

  • Cloud Dataflow is a difficult service to describe
    • Pub/Sub > Dataflow > BigQuery (Streaming)
    • Pub/Sub > Dataflow > Cloud Storage (Streaming - files)
    • Cloud Storage > Dataflow > Bigtable/CloudSpanner/Datastore/BigQuery (Batch - Load data into databases)

12. Data Architectures in GCP

Loose Coupling with Pub/Sub

  • Whenever you want to decouple a publisher from a subscriber, consider Pub/Sub

Date Formats

  • Structured: Tables, Rows and Columns (Relational)
    • Cloud SQL, Cloud Spanner, BigQuery
  • Semi Structured: Flexible Schema
    • Cloud Firestore/Datastore
  • Unstructured: Video, Audio, Image, Text, Binary files
    • Cloud Storage

Cloud Dataproc

  • Cloud Dataproc: Managed Spark and Hadoop service

Big Data Flow - Batch Ingest

  • Use extract, transform, and load (ETL) to load data into BigQuery
  • Dataprep: Clean and prepare data
  • Dataflow: Create data pipelines (and ETL)
  • Dataproc: Complex processing using Spark and Hadoop

Steaming Data

  • Pub/Sub: Receive messages
  • Dataflow: Analyze, aggregate andfilter data
  • For pre-defined time series analytics, storing data in Bigtable
  • For ad hoc complex analysis, prefer BigQuery

IOT

  • IoT Core: Manage IoT (registration, authentication, and authorization) devices
  • Pub/Sub: Durable message ingestion service (allows buffering)
  • Dataflow: Processing data (ETL & more..)
  • Data Storage and Analytics:
    • Make IOT data available to mobile or web apps => Datastore
    • Execute pre-defined time series queries => Bigtable
    • More complex or ad hoc analytics/analysis => BigQuery

Data Lake

  • Single platform with combination of solutions for data storage, data management and data analytics

  • Storage
    • Cloud Storage (low cost + durability + performance + flexible processing)
  • Data Ingestion
    • Streaming data - Cloud Pub/Sub + Cloud Dataflow
    • Batch - Transfer Service + Transfer Appliance + gsutil
  • Processing and analytics
    • Run in-place querying using SQL queries using BigQuery or (Hive on Dataproc)
  • Data Mining and Exploration
    • Clean and transform raw data with Dataprep
    • Use Cloud Datalab (data science libraries such as TensorFlow and NumPy) for exploring

Data Governance

  • Bad data: Bad data leads to poor business decisions
  • Data leaks: Data leaks can lead to a reputation loss

Dataplex

  • Dataplex is a Data Mesh: Unified dashboard with visibility into all data assets (data lakes, data warehouses, ..)

13. API Management in GCP

API Management

  • Apigee API Management: Comprehensive API management platform
  • Cloud Endpoints: Basic API Management for Google Cloud backends
  • API gateway: Newer, Simpler API Management for Google Cloud backends

14. Trust and Security with GCP

Cloud Security

  • Control: Decide who gets access
  • Compliance: Follows legal rules
  • Confidentiality: Keeps information secret
  • Integrity: Ensures data stays accurate
  • Availability: Ensure apps & data are available always

Enhanced Security with 2SV

  • 2 Step Verification (2SV): Add a 2nd step to verify user
  • Make 2SV Mandatory: For Google Cloud accounts

GCP Security Offerings

  • KMS: Create and manage cryptographic keys (symmetric and asymmetric). Control their use
    in your applications and GCP Services
  • Secret Manager: Manage your database passwords, your API keys securely
  • Cloud Data Loss Prevention: Discover, classify, & mask sensitive data
  • Cloud Armor: Protect your production apps (at run time) from denial of service and common web attacks

  • Web Security Scanner: Identify vulnerabilities by running security tests
  • Binary Authorization: Ensure that only trusted container images are deployed to Google Cloud
  • Container Threat Detection: Detects container runtime attacks
  • Security Command Center: Get a consolidated picture of security in Google Cloud

Zero Trust Security Model

  • Zero Trust - “No person or device should be trusted by default, even if they are already inside an organization’s network”

15. ML in GCP

ML in GCP - Pre-Trained

  • Speech-to-Text API: convert speech into text
  • Text-to-Speech API: convert text into speech
  • Translation API: Translate texts into more than one hundred languages
  • Natural Language API: Derive insights from unstructured text
  • Cloud Vision API: Recommended for generic usecases

ML in GCP - Custom Models

  • AutoML: Build custom models with minimum ML expertise and effort
    • AutoML Vision: Build custom models based on Images
    • AutoML Video Intelligence: Add labels to Video
    • AutoML Tables: Automatically build models on structured data
  • BigQuery ML: Build ML models using Queries
  • Vertex AI: Build & deploy ML models faster

16. Cloud Native

Cloud Native Pillars

  • Microservices: Fix issues and deliver new features quickly
  • Containers: Portable & Lightweight
  • Container Orchestration: Kubernetes (GKE) - Auto Scaling, Load Balancing, Self Healing, Zero Downtime Deployment etc
  • DevOps (Dev + Ops, CI/CD, IaC): Increased automation of processes

Container Compute Examples

  • Cloud Run: Develop and deploy highly scalable containerized applications
  • Google Kubernetes Engine: Orchestrate containerized microservices on Kubernetes
  • Anthos: Manage Kubernetes Clusters in Multi-cloud and On-premises

Serverless Examples

  • Cloud Functions: Serverless compute for event-driven apps
  • Cloud Run: Run isolated containers, without orchestration (Serverless)
  • Cloud Firestore: Apps needing quickly evolving structure (schema-less)
  • Cloud Dataflow: Serverless Stream and Batch processing using Apache Beam
  • Cloud Pub/Sub: Realtime Messaging in the cloud. Pay for number of messages
  • BigQuery: Relational OLAP, Data warehousing & BigData workloads

17. Cost Management in GCP

CapEx vs OpEx

  • Capital Expenditure (CapEx): Money spent to buy infrastructure
  • Operational Expenditure (OpEx): Money spent to use a service or a product

Pricing Calculator

  • Pricing Calculator: Estimating the cost of a Google Cloud solution

GCP Cost Management

  • Cost Management: Tools for monitoring, controlling, and optimizing your costs

18. GCP review

Basic Compute Services

  • Compute Engine: Use VMs when you need control over OS OR you want to run custom software
  • Preemptible VMs: Short lived VMs for non time-critical workloads
  • Sole-tenant Nodes : Dedicated physical servers
  • VMware Engine: Run VMware workloads in Google Cloud
  • Managed Instance Groups: Create multiple Compute Engine VMs
  • Cloud Load Balancing: Balance load to multiple instances of an application or a service

Managed Compute Services

  • App Engine: PaaS. Deploy web apps and RESTful APIs quickly
  • Cloud Run: Run isolated containers, without orchestration (Serverless)
  • Kubernetes Engine: Managed Kubernetes Service. Provides container orchestration
  • Cloud Functions: Serverless compute for event-driven apps
  • Anthos: Manage Kubernetes Clusters in Multi-cloud and On-premises
  • Firebase: Google’s mobile platform. Build Apps for iOS, Android, the web, C++, and Unity

Storage

  • Persistent Disk: Block Storage for your VMs
  • Local SSD: Local ephemeral block storage for your VMs
  • Cloud Filestore: File shares in the cloud
  • Cloud Storage: Object storage in the cloud

Databases

  • Cloud SQL: Regional Relational OLTP database (MySQL, PostgreSQL, SQL server)
  • Cloud Spanner: Global Relational OLTP database. Unlimited scale and 99.999% availability for global applications with horizontal scaling
  • Cloud Firestore: Apps needing quickly evolving structure (schema-less)
  • Cloud BigTable: Large databases(10 TB - PBs). Streaming (IOT), analytical & operational workloads. NOT serverless
  • Cloud Memorystore: In memory databases/cache. Applications needing microsecond responses

Streams, Analytics, Big Data

  • Cloud Pub/Sub: Realtime Messaging in the cloud
  • BigQuery: Relational OLAP databases. Datawarehousing & BigData workloads
  • BigQuery ML: Simplified Machine Learning using data in BigQuery
  • Cloud Dataflow: Serverless Stream and Batch processing using Apache Beam (open-source)
  • Cloud Dataproc: Managed Service for Spark and Hadoop. Not serverless
  • Cloud Data Fusion: Visually manage your data pipelines
  • Data Studio: Visualize data
  • Looker: Enterprise Business Intelligence

Migration

  • Database Migration Service: Migrate to Cloud SQL
  • Storage Transfer Service: Online Transfer to Cloud Storage
  • Transfer Appliance: Physical transfer using an appliance
  • Migrate for Compute Engine: Migrate VMs and VM storage to GCE
  • Migrate for Anthos: Migrate VMs to GKE containers
  • BigQuery Data Transfer Service: Migrate your analytics data