G Suite groups can be used for various reasons, such as forming mailing lists and assigning permissions. G Suite organizational units (OUs) contain users and devices that share similar settings. Explore G Suite groups and OUs.
Discover how G Suite mail settings, such as routing, whitelisting, and filtering, improves user experience. Also, explore how calendar sharing and creating calendar resources, such as conference rooms, makes scheduling efficient.
Google Cloud Platform (GCP) provides cost-effective, scalable, high-performing, and secure solutions for business-critical workloads. Explore GCP's value, including storage, databases, big data, and identity and access management (IAM).
You can't build a house without tools and materials. Similarly, you can't build a DevOps infrastructure without the tools and materials needed to support developer needs. In this course, learn about the basics of the Google Cloud Platform (GCP). Explore how GCP compares to other cloud platforms and learn about different elements of the platform, such as computing, storage, and networking. Next, investigate tools and services like identity management, databases, analytics, and artificial intelligence. Finally, examine the relationships between the GCP and container technologies such as Docker, clustering container technologies such as Kubernetes, and orchestration technologies such as Jenkins. Upon completion, you'll be able to identify how elements of Google Cloud Platform (GCP) support a DevOps framework.
In order to be a competent practitioner of Google Cloud technologies, you will need to get up close and personal with the various tools used to create a DevOps infrastructure. In this course, you will discover the different components of the Google Cloud Platform (GCP). Begin by exploring the App Engine, Compute Engine, and Cloud VMware Engine. Then, learn how to enable and manage data storage such as Cloud Storage, Filestore, and databases such as Firebase, Bigtable, Datastore, and Firestore. You will delve into the mechanics of cloud networking using a content delivery network (CDN) and Cloud Router. Finally, investigate automation tools such as Cloud Build, as well as tools for monitoring and logging using Cloud Operations. After completing this course, you'll be able to implement the GCP elements that support a DevOps framework
Google Cloud SQL is a cloud-hosted, fully-managed database service on Google Cloud Platform (GCP). With Cloud SQL, users focus on modeling and managing the data in the systems, and GCP takes care of the management of the infrastructure and platform. Through this course, learn about Cloud SQL's architecture and features and how to set up a MySQL instance on this service. Explore Cloud SQL and some of its significant features and use cases. Next, practice provisioning a MySQL database instance and connect to Cloud SQL from an external client. Finally, discover some important configuration options for a Cloud SQL instance. Upon completion, you'll be able to name Cloud SQL's features and use cases and provision a MySQL instance.
Cloud SQL is a relational database service that has a lot to offer. Features such as high availability and more are built into the service, which has been widely adopted across industries. Through this course, learn about advanced Cloud SQL topics. Practice provisioning Cloud SQL instances that are running both PostgreSQL and MySQL database engines and configuring a Cloud SQL instance for high availability. Next, discover how to test a failover process and how read replicas can be provisioned. Finally, learn how to migrate from a VM MySQL database to a MySQL instance in Google Cloud SQL and integrate a Cloud SQL instance with App Engine and BigQuery. Upon completion, you'll be able to provision instances of Cloud SQL using different database engines.
Google Cloud Platform offers several solutions to streamline any enterprise while keeping costs low. Explore these benefits, including how to navigate GCP and choose between the various data processing products it provides.
Executing Dataproc implementations with big data can provide a variety of methods. Examine Dataproc implementations with Spark and Hadoop using the cloud shell and introduce BigQuery PySpark REPL package.
BigQuery is an essential tool for any data analyst using Google Cloud Platform. Explore BigQuery and examine several operations including multiple table queries, nested and repeated fields and building BigQueries.
Feature engineering can be an essential tool in applied machine learning when enhancing a dataset. Explore concepts of feature engineering, including areas of streaming architecture and implementations.
Google Cloud Platform (GCP) offers several tools for serverless application development and deployment. Use this course to learn how to take advantage of GCP serverless compute, storage, and app services. Investigate the GCP tools for hosting applications and examine how Google Cloud Functions and App Engine work. Explore the pros and cons of Firebase, the benefits of using Cloud Run, the use cases of Cloud Datastore, and the GCP serverless products for building apps for analytics. Moving along, explore GCP serverless microservices and how serverless deployments can improve DevOps productivity. Finally, learn how to deploy Node.js Cloud Functions, applications to App Engine, and containers from a Container Registry repository to Cloud Run. Store and query data in Firestore in Datastore mode and create streaming pipelines. When you're done, you'll be able to use GCP tools to develop scalable serverless applications efficiently and successfully.
Cloud Run on the Google Cloud Platform (GCP) enhances the experience of building and deploying scalable serverless applications. Use this course to become familiar with using Cloud Run on a GCP-powered, fully managed serverless platform. Explore Cloud Run architectures, the role of Knative, and how Cloud Run and Cloud Run for Anthos differ. Investigate the lifecycle of a Cloud Run container, services for defining serverless service workflows, and GCP's load balancing and autoscaling capabilities. Differentiate between Cloud Tasks and Cloud Scheduler and outline best practices for designing, implementing, testing, and deploying Cloud Run services. After completing the course, you'll be able to package a simple Node.js application into a container image, deploy it to Cloud Run, use Cloud Build triggers to automate builds and deployments to Cloud Run, set up Cloud Code extension on IntelliJ, and create Cloud Run services using Cloud Code's starter templates.
Google Cloud Platform is a suite of cloud services that provides reliable and highly scalable cloud computing services that help its users store data, build, test, and deploy apps. It provides computing services for backend, mobile and web solutions using the internet. You'll begin with exploring Google Cloud Platform and the various Google Cloud services available matching them to business needs, such as Virtual Machines, Kubernetes and Cloud Functions. You'll learn how to create a GCP account and navigate the console, create a billing account, establish budgets and alerts, setup billing exports, link accounts to Google Cloud projects and use the GCP pricing calculator. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Cloud Platform reduces costs while increasing agility and capturing new opportunities. In this course, you'll begin by learning how to use using Google Cloud Shell. You'll explore how to install and use Cloud tools, the gcloud CLI, Google Cloud SDK, and other utilities that come pre-installed. Next, you'll learn about various Google Cloud database services such as Cloud SQL, which provides a cloud-based alternative to local MySQL, PostgreSQL, and SQL Server databases. You'll examine Cloud Spanner, a fully managed relational database with unlimited scale. You'll move on to learn about Cloud Bigtable, a NoSQL wide-column store for large scale, low latency workloads. Finally, you'll learn about BigQuery, Dataproc, and Cloud Marketplace. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Cloud Resource Manager allows you to hierarchically manage resources by project, folder, and organization. In this course, you'll learn about Google Cloud Resource Manager and the resource hierarchy, as well as how to set up an organization resource to ensure projects will follow the organization's life cycle on Google Cloud. You'll then examine how to create and manage a Google Cloud project and an organization. You'll explore how to create labels and tags, move and migrate projects, and enable APIs within projects. Next, you'll learn how to manage Google Cloud folders by learning how to set roles and permissions, create folders, configure access, and create a project in a folder. Finally, you'll learn to enable and view audit logs, and set up and manage Cloud Identity. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Cloud Load Balancer is a fully distributed, software-defined managed service that offers a single IP address to serve as the front end, and automatic intelligent autoscaling of the back end. This means you don't need to manage physical load balancing infrastructure. In this course, you'll learn about Google Cloud Load Balancer, how to identify the key components and choose which one is best for your implementation, and how to manage access control using Identity and Access Management. You'll also explore how to run and scale services behind internal and external IP addresses, use a network load balancer to distribute traffic, distribute SSL and TCP traffic, and distribute traffic to applications running on back-end instances. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Cloud gives you compute and hosting options, and you can work in a serverless environment, use a managed application platform, leverage container technology, or build your own cloud-based infrastructure for the most control and flexibility. In this course, you'll begin by learning about Virtual Machine instances and the instance life cycle, as well as how to create different instances such as preemptible and Windows Server instances. Next, you'll examine how to provision sole-tenant nodes, generate SSH keys, connect to Linux and Windows VM instances using Google Cloud Console, and use the gcloud command-line tool with SSH keys. Finally, you'll learn how to create a managed instance group and an instance template. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Cloud Engine is the Infrastructure as a Service component of the Google Cloud Platform. It is built on the same infrastructure that runs Google's search engine, Gmail, and even YouTube. In this course, you'll start by learning how to setting up Virtual Machine Manager. You'll explore how to manage a VM instance by going through its life cycle, including starting, stopping, suspending, and resuming an instance. You'll then learn how to update an instance by using gcloud or API, as well as how to connect to it by using Secure Shell and Remote Desktop Protocol. You'll also examine how to create a VM with attached GPUs. Next, you'll learn to setup and use OS inventory management, create and view snapshots and images, and use instance groups and templates. Finally, you'll explore how to query the metadata server to retrieve information, and how to access and request quota increases. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Cloud is the birthplace of Kubernetes, an open-source system used to deploy, scale, and manage containerized applications. In this course, you'll begin by learning about Google Kubernetes Engine, the different modes of operations, and the types of clusters that can be created. Next, you'll examine how to create different GKE clusters and explore Kubernetes pods and different types of controller objects. You'll examine GKE security features, the different aspects of GKE networking, and the storage options for applications running on GKE. Finally, you'll learn to deploy a GKE cluster and a Docker container image on a GKE cluster, create a Google Cloud service account, and configure GKE monitoring and logging. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Kubernetes Engine is a secured and fully-managed Kubernetes service that features autopilot mode of operation, pod and cluster autoscaling, and enterprise-ready containerized solutions. Use this course to get familiar with using the Google Kubernetes Engine dashboard. Start by learning how to manage clusters and controlling access to them. Next, you'll examine the use of the dashboard to perform other operations, such as creating and using volumes with deployments, creating secrets to store sensitive data, and using Config Connector for Kubernetes. You'll also explore how to view the container image repository, manage node pools, and implement pod deployments. You'll then discover the creation of StatefulSets, DaemonSets, and a ConfigMap. Finally, you'll learn to use Persistent volumes and dynamic provisioning to manage durable storage in your clusters. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google App Engine is a Platform as a Service and a cloud computing platform that provides web app developers and enterprises with scalable hosting services. In this course, you'll learn about the different Google App Engine environments that are available, as well as Google App Engine feature deprecations and how to install SDKs. Next, you'll explore how to setup a Cloud Run environment and Cloud Functions. You'll then examine how to deploy App Engine using gcloud, deploy a container image to Cloud Run, and deploy Cloud Functions from your local machine, GitHub, and Bitbucket. Finally, you'll learn how to deploy an app that receives Google Cloud events and setup Pub/Sub notifications for Cloud Storage. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google App Engine is a fully managed serverless environment that manages infrastructure concerns by provisioning servers and scaling your instances based on demand, allowing you to focus on code. You'll begin by learning how to use traffic splitting to distribute traffic across multiple services, use Traffic Migration to route requests, map custom domains to your apps, and secure your custom domain with SSL. Next, you'll learn how to manage your app resources, update your scaling configurations, set parameters with the API, and deploy versions of your app to App Engine. Finally, you'll learn how to create and view a Cloud Run service, and view a list of revisions to a service. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Cloud Storage is an infrastructure as a service (IaaS) public cloud storage platform that can house large unstructured data sets. Use this course to learn more about Google Cloud Storage along with its features and functions. Start your journey by exploring Object Lifecycle Management, different Cloud Storage options, and the best practices associated with cloud storage. You'll then examine storage buckets and how they are used. You'll also learn to identify the naming guidelines, locations, and requirements for domain-named buckets. Finally, you'll discover how to create a storage bucket using Google Cloud console and the gsutil tool, change the storage class of objects as well as buckets, and migrate a database to Cloud SQL. After you have completed this course, you'll have the skills and knowledge to implement Google Cloud storage and prepare for the Google Cloud Associate Cloud Engineer certification.
Google Cloud Storage is a service for storing and retrieving your objects in Google Cloud. It can be used for serving web site content, archival and disaster recovery, and distributing large data objects. In this course, you'll begin by exploring how to list Cloud Storage buckets in a project and how to move objects between them. You'll learn to label and convert buckets, setup lifecycle management policies, and execute queries. Next, you'll examine how to use BigQuery to estimate storage and query costs, restore and back up an instance using Cloud SQL Data Management, and use the managed export and import service. Finally, you'll learn how to use Dataproc, the Dataflow monitoring interface, and BigQuery to review the status of jobs. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Virtual Private Cloud provides virtualized network functionality to Google Compute Engine virtual machine instances, Google Kubernetes Engine clusters, and the Google App Engine flexible environment. You'll begin by learning about Virtual Private Cloud and the creation of VPC networks and subnets. You'll learn how to launch Google Compute Engine instances with custom network configurations and configure Private Google Access. Next, you'll discover how to assign tags to VMs, create and manage routes, create ingress and egress firewalls, and use Cloud VPN to create a VPN between Google VPC and external networks. Finally, you'll learn how to create and launch Deployment Manager templates, add subnets to existing VPCs, use gcloud to expand subnets, and reserve new static IP addresses. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Cloud's operations suite, formerly Stackdriver, is used to monitor, log, diagnose, and improve application performance on your Google Cloud environment. In this course, you'll learn about Cloud Monitoring, including how to install the Cloud Monitoring agent on a VM, manage the agent on multiple VMs, and create custom metrics. Next, you'll explore how to create a resource group, use the dashboard to manage alert policies, create a workspace to organize, and manage the monitor information. You'll learn about Cloud Logging and logs-based metrics, as well as how to create charts and alerts using the Google Cloud Console, view and filter logs in Logs Explorer, and search logs with Legacy Logs Viewer. Finally, you'll examine how to create and manage log sinks, use the Cloud Error Reporting console, use Cloud Trace and Cloud Debugger, and view the Google Cloud status dashboard. This course is one of a collection of courses that prepares learners for the Google Cloud Associate Cloud Engineer certification.
Google Cloud Identity and Access Management (IAM) is an indispensable tool for Cloud Engineers. It offers centralized access control and visibility of all Google Cloud resources, providing a unified view of security policies across the entire organization. Use this course to explore how IAM works in general and in Google Cloud. Learn how to manage IAM user roles, view IAM role assignments, and identify grantable roles. Practice working with custom roles, service accounts, and IAM audit logs. Lastly, examine identity federation in IAM. Learn how to access resources using workload identity and configure conditional access. By the end of the course, you'll be able to manage all resource authorization using Google Cloud IAM. This course is part of a collection that prepares learners for the Google Cloud Associate Cloud Engineer certification.
With the serverless execution environment Google Cloud Functions, application code executes in a fully managed environment, removing the need to provision infrastructure or manage servers. Using this course, explore the use of Google Cloud Functions, the runtimes it supports, and its frameworks. Examine the features of Cloud Firestore and Firebase Realtime Database along with some common Cloud Functions tests. Create HTTP functions using Node.js, Python, Java, and Go and deploy Cloud Functions from your local machine, source repository, and GCP console. Moving along, write background Cloud functions using Cloud Pub/Sub and Cloud Storage triggers and unit tests for the HTTP-triggered and event-driven functions. Use Log Explorer for a variety of tasks and integrate Cloud functions with ReactJS-based applications. When you're done, you'll be able to use Google Cloud Functions to write simple, single-purpose functions attached to events emitted from cloud infrastructure and services.
App Engine is a fully managed serverless platform that provides a managed environment and custom runtimes to build highly scalable applications in Node.js, Java, Ruby, C#, Go, Python, and PHP without any infrastructure concerns. Use this course to become familiar with using Google App Engine. Learn how to deploy multiple API versions to the same App Engine version and structure the services and related resources of applications for App Engine. Explore the different methods for splitting traffic in App Engine, the various mechanisms for securing web applications on App Engine, and the key features of Google Cloud's operations suite. Finally, work with web apps, data, and testing in App Engine and configure monitoring and logging for Google Serverless applications. When you're done with this course, you'll know when and how to use App Engine during serverless implementation.
Google Cloud Platform (GCP) is a cloud computing suite offering services, such as machine learning, storage, big data, analytics, IoT, and DevOps. Explore GCP's Container Engine, Compute Engine, App Engine, and basic networking services.
Google Cloud Platform (GCP) offers several storage and database solutions based on varying workloads and business needs. Explore some of these solutions, including Cloud Datastore, Cloud Storage, Cloud SQL, Cloud Bigtable, and BigQuery.
Explore the features, benefits, and solutions provided by Google Cloud Platform (GCP) for end-to-end CloudOps implementation in this 14-video course, which examines features of Cloud Build and how to work with Cloud Build to automate workflows and application deployment. Learn how to create a source code repository by using Google Cloud Console and gcloud command line tool. Also, learn how to build and implement delivery pipelines with tools and services provided by GCP by exploring the technical, process, measurement, and cultural capabilities of GCP that drive high software delivery with CloudOps, along with the prominent solutions, benefits, and approaches of implementing end-to-end GCP CloudOps. Examine features provided by GCP's Cloud Build, along with how to use Cloud Build and cloud source repositories and GitHub to automate App Engine deployment, the prominent tools provided by GCP, and how to build configuration management workflows in GCP. Finally, discover how to create continuous delivery pipelines by using Google Kubernetes Engine, cloud source repositories, Cloud Build, and Spinnaker for Google.
In this 18-video course, learners explore features of Google Cloud Platform (GCP), Google Kubernetes Engine (GKE), best practices for operating containers, and how to build cloud-native applications with CloudOps methodology. First, compare Google Cloud Source Repository with GitHub, and see how automated deployment capabilities of Google Cloud Deployment Manager compare with Terraform. Then delve into GCP's machine learning (ML), artificial intelligence, and analytical capabilities and essential design patterns for connecting GCP with other cloud platforms. Discover how to create and configure GKE clusters; deploy applications across multiple Kubernetes clusters; create repositories and manage code with Google Cloud Source Repository; and deploy applications from Cloud Source Repository to App Engine. Next, learn how to automate the configuration of GCP resources and application deployment with Google Cloud Deployment Manager, create virtual machine (VM) instances on GCP; build applications with Terraform; and configure AutoML and BigQuery to manage large volumes of data and build ML models. Finally, learners discover how to set up fully-managed real-time messaging environments, and transfer data between GCP and other Cloud Service providers.
With Google Cloud SQL, connections can be made using the various SDKs to post and view database information. Learn how to install the Google Cloud SDK and manage Cloud SQL instances using the Cloud SQL API.
Businesses and IT leaders across the globe are replacing their legacy, on-premises technology by moving to the cloud. Google Cloud Platform protects your data, applications, infrastructure and so much more. Explore the benefits of moving to the Google Cloud Platform. Examine the microservices architecture and develop microservice apps. Discover the scaling velocity characteristics of Infrastructure as a Service (IaaS), Containers as a Service (CaaS), and Platform as a Service (PaaS). Then, use Cloud Storage, Cloud Bigtable, Cloud Spanner, and Cloud SQL to define a key structure for high-write applications. Learn how to set session lengths for Google Cloud services and how to deploy and secure application programming interface (API) services. Finally, identify and implement Google Cloud best practices. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Organizations continue to take advantage of Google Cloud Platform's powerful network to deliver enterprise-class applications. Google uses a secure by design infrastructure, built-in protection, and a global network that helps organizations protect their information. In this course, learn about Google Cloud security, security mechanisms, container scanning, and workload identity federation. Discover how to implement Binary Authorization using Cloud Build and GKE, manage notifications with Secret Manager, and use JSON web tokens and OAuth 2.0 to authenticate services. Finally, practice authenticating using asynchronous or synchronous means between services, control communication between services, and use certificate-based authentication to protect resources. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Cloud management tools are tailored to provide organizations with visibility, accountability, and control over their business, reducing costs and complexity while increasing productivity and predictability. In this course, learn about the various Google Cloud management tools, such as Firestore, Cloud Spanner, Cloud Bigtable, Cloud SQL, and Cloud Storage. Explore data retention and the database options available for structured and unstructured data. Discover NetApp Cloud Volumes Services and identify Cloud Storage best practices. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Many organizations seek to determine what their return on investment will be if they upgrade their environments, but quantifying the future impact of modernization initiatives can be challenging. Google Cloud's application modernization solutions can help you be more innovative while reducing your costs. In this course, learn about Google Cloud's application modernization solutions, including the Google Cloud Application Modernization Program (CAMP), in addition to hybrid and multicloud applications, API management, mainframe modernization, and more. Next, explore the benefits of using Google's managed service providers, refactoring applications from monolith to microservices, and designing scalable and resilient applications. Finally, learn how to deploy stateful applications. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google App Engine requires zero configuration and no server management. It also offers flexibility that allows developers to focus on other applications and processes and write code. In this course, you'll learn how to build an application on Google App Engine, create a project using resource manager API, download and install gcloud CLI, and use Google Cloud Console and Cloud Shell. You'll explore Google developer tools, use Cloud Code to debug applications and Skaffold to automate workflows, set up your development environment on Go, and finally use the local development server. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Using Google Cloud Platform maximizes developers' productivity by offering various tools to assist in software development. Google Cloud Code offers features such as remote debugging, reduced context switching, YAML authoring support, and much more. Learn about Google Cloud Code, deploying Kubernetes apps, and using Cloud Run services. Discover how to use built-in algorithms and identify design patterns. Learn to use Cloud Debugger to inspect applications and Cloud Profiler to identify parts of applications consuming the most resources. Finally, learn about Google DevOps and software development methodologies. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Organizations are benefiting from using Google Cloud Testing as it is cost-effective, reduces resource requirements, allows for availability and scalability, and enables a quicker testing process. In this course, learn about Google Cloud Testing and test HTTP and event-driven Cloud Functions. Discover how to configure a CI/CD platform and perform unit, integration, performance, and load testing. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Organizations continuously look for ways to maximize their cloud footprint, and containerization has become a focal point in this evolution. Google Cloud Build is a managed service that enables developers to build, test, and deploy containers quickly across all languages. In this course, learn about Google Cloud Build and how to use it to automate builds. Next, discover how to use community-contributed and custom builders, create a code repository, store build artifacts, and automate App Engine deployments. Finally, practice using Container Registry to store container images, set up Jenkins on Google Kubernetes Engine (GKE), and improve CI/CD. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Deployment strategies are used to implement changes or upgrades in your applications without interruption or downtime for the user. Google Cloud deployment strategies allow for zero downtime, instant rollbacks, and environment separation. In this course, explore strategies for application deployment and testing, an introduction to Google Cloud Deployment Manager, and how to use Cloud Build to automatically deploy Cloud Run. Next, practice using Spinnaker to integrate existing workflows, Tekton to create CI/CD, and Anthos Config Management for configuration and policy management. Finally, learn how to implement various deployments, such as blue/green, traffic-splitting, rolling, and canary. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Compute Engine (GCE) offers various advantages for deploying your applications, such as throughput, stability, pricing, backup, and security. Containers on Compute Engine instances allow you to conveniently run your apps on host virtual machines (VMs) with fewer dependencies. In this course, learn about the different types of containers that can be run on Compute Engine instances. Next, discover how to install applications and manage service accounts for VMs, bootstrap applications on Compute Engine, and configure and manage sinks. Finally, practice exporting metrics, using operating system images to create boot disks for instances, and configuring a Binary Authorization policy with Google Kubernetes Engine (GKE). This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Kubernetes Engine is one of the leading hosted container orchestration solutions. It provides a hosted environment for running containerized applications and managing workloads to your specifications, while integrating with other Google Cloud services for secure, highly available, and scalable deployments. In this course you'll explore Google Kubernetes Engine (GKE), deploy a containerized application to GKE, and control access to cluster resources using role-based access control (RBAC) and identity and access management (IAM). Next, you will practice configuring Kubernetes namespaces, identify workload profiles and specifications, and build container images using Cloud Build. You will configure Kubernetes network policy and Kubernetes services and explore how to manipulate the GKE Pod life cycle. Finally, you'll explore how Kubernetes resources and configurations are defined. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Cloud Functions is a serverless, event-driven computing service that lets you treat all cloud services as building blocks. Developers can use it to create and implement functions without needing to provision other infrastructures like servers, storage, and other resources. In this course, learn about Google Cloud Functions and its triggers and events, including Pub/Sub and Cloud Storage triggers. Next, discover how to write and deploy Cloud Functions, the types of triggers for calling Cloud Functions, and use HTTP functions. Finally, practice creating an event that triggers CloudEvent and secure Cloud Functions using identity and network-based approaches. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Cloud service accounts are special account types used to represent non-human functions that authenticate/authorize to gain access to data in Google APIs. Examples of service accounts include running workloads on virtual machines, workstations, or data centers. In this course, learn about the different types of service accounts, create and manage service accounts using the IAM API, and work with the different types of service account keys. Next, discover how to create short-lived service account credentials and manage impersonation and the Service Account Credentials API. Finally, explore how to monitor and view usage patterns and learn about the principle of least privilege. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Cloud Storage is a platform that houses unstructured data sets, typically used for primary or infrequently accessed data. Data and storage services provide flexibility and reliability in a secure location which enables customers to transition to lower-cost classes easily, allows for multiple redundancy options, provides usable archival storage, and provides storage classes for any workloads. In this course, you will learn about application integration and identify import and export best practices. You will learn how to import and export using SQL and CSV and check the status of import and export operations. Finally, you will learn to connect to Cloud SQL, Cloud Spanner, Firestore, and Cloud Bigtable data stores. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Cloud compute services allow you to create and run customizable virtual machines. It automatically deploys, scales, and manages your containers with Google Kubernetes Engine (GKE) or Cloud Run. You can migrate your apps at your own pace with the ability to move directly to virtual machines or automatically to containers. In this course, learn about service discovery and how to implement it in GKE and Compute Engine. Next, discover how to access instance metadata. Finally, explore how to authenticate users using Identity-Aware Proxy and OAuth 2.0 web flow and cloud APIs with workload identity. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Cloud Platform (GCP) services include programmatic interfaces called Google Cloud APIs. Google Cloud APIs allow you to easily connect computing, networking, storage, and machine learning data to your applications. In this course, learn about Google APIs, enable APIs in your Google Cloud projects, and practice making API calls using REST APIs, a Cloud Client Library, gRPC, and the Google APIs Explorer. Next, discover how to send batched requests into a single HTTP request, restrict returned data, and paginate results. Finally, explore error handling for Google APIs, work with cached query results, and use service accounts to make API calls. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Cloud Compute Engine allows you to create and run virtual machines (VMs). Compute Engine instances can be used to run the public images for both Linux and Windows Server. In this course, learn about managing Google Compute Engine VMs. Next, discover how to debug custom VMs, debug using a serial console, and debug a failed VM startup. Finally, explore how to install the Cloud Logging agent on individual VMs, work with the Logs Explorer to analyze logs, and view a resource utilization report. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
Google Kubernetes Engine (GKE) provides a managed environment for deploying, managing, and scaling containerized applications. GKE consists of multiple machines grouped to form a cluster. In this course, you'll explore the use of GKE workloads, and configure Cloud Logging and Cloud Monitoring. You'll access the monitoring dashboard to view container life cycle events, troubleshoot issues with deployed workloads, and view GKE logs. You will explore how to manage GKE metrics, use custom metrics to capture application-specific data, and identify external metrics. Finally, you will see how to use Pub/Sub to receive notifications from your GKE clusters and autoscale your GKE clusters. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
The Google Performance Dashboard gives you visibility into all of the Google Cloud network. It shows you the performance of the entire network as well as any of your project's resources. In this course, you'll learn about the Google Performance Dashboard, create a monitoring dashboard, write custom metrics, create log-based metrics, and use Cloud Debugger. You'll review stack traces for error analysis, setup service log exports to BigQuery, view logs in the Google Console, monitor health and performance using Cloud Monitoring, and review application performance using Cloud Trace. You'll also use Prometheus to monitor and alert you on your workloads and OpenTelemetry to monitor and analyze your applications. Finally, you'll measure app performance using Cloud Profiler and use documentation, forums, and Google Cloud support. This course is one of a collection that prepares learners for the Google Professional Cloud Developer exam.
For every colony of bees, there are many workers and only one queen. In companies that are deploying applications to the cloud, that quintessential VIP role is the cloud architect. In this course, you will explore the role of a cloud architect, including job responsibilities, main activities, and key skills. Discover how the cloud architect role differs from other cloud-focused roles, such as a cloud developer or DevOps engineer. Then, focus on the Google Professional Cloud Architect exam to identify exam preparation resources and investigate practice questions to see what the exam might be like. This course is one of a collection that prepares learners for the Google Professional Cloud Architect exam.
Google Cloud architects must use good judgment in assessing technical solutions and address key factors for business success when deploying software in the cloud. In this course, you will learn how different use cases can affect the scope of a project. Explore ways to balance different needs, including key features in application design, cost considerations, integration with other systems, accessing large quantities of data, governance through compliance and regulations, and security. Learn about ways to measure the success of a cloud deployment. Finally, explore how this course relates to commonly seen exam questions. This course is one of a collection that prepares learners for the Google Professional Cloud Architect exam.
Though a cloud architect must focus on the business needs of an organization, they will be wildly ineffective without a thorough understanding of the technical requirements of a project that are used to meet those business needs. In this course, you will explore high availability, scalability, and reliability - the three main categories of those technical requirements. You will discover how each of those requirements is integrated into decisions made regarding the compute, storage, networking, and application design decisions. Finally, you will examine use cases that you can expect to encounter in an exam environment. This course is one of a collection that prepares learners for the Google Professional Cloud Architect exam.