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Nettundervisning 3 dager 27 000 kr
In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. [+]
Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. Objectives This course teaches participants the following skills: Use best practices for application development Choose the appropriate data storage option for application data Implement federated identity management Develop loosely coupled application components or microservices Integrate application components and data sources Debug, trace, and monitor applications Perform repeatable deployments with containers and deployment services Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Best Practices for Application Development -Code and environment management-Design and development of secure, scalable, reliable, loosely coupled application components and microservices-Continuous integration and delivery-Re-architecting applications for the cloud Module 2: Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK -How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK-Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials Module 3: Overview of Data Storage Options -Overview of options to store application data-Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner Module 4: Best Practices for Using Cloud Datastore -Best practices related to the following:-Queries-Built-in and composite indexes-Inserting and deleting data (batch operations)-Transactions-Error handling-Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow-Lab: Store application data in Cloud Datastore Module 5: Performing Operations on Buckets and Objects -Operations that can be performed on buckets and objects-Consistency model-Error handling Module 6: Best Practices for Using Cloud Storage -Naming buckets for static websites and other uses-Naming objects (from an access distribution perspective)-Performance considerations-Setting up and debugging a CORS configuration on a bucket-Lab: Store files in Cloud Storage Module 7: Handling Authentication and Authorization -Cloud Identity and Access Management (IAM) roles and service accounts-User authentication by using Firebase Authentication-User authentication and authorization by using Cloud Identity-Aware Proxy-Lab: Authenticate users by using Firebase Authentication Module 8: Using Google Cloud Pub/Sub to Integrate Components of Your Application -Topics, publishers, and subscribers-Pull and push subscriptions-Use cases for Cloud Pub/Sub-Lab: Develop a backend service to process messages in a message queue Module 9: Adding Intelligence to Your Application -Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API Module 10: Using Cloud Functions for Event-Driven Processing -Key concepts such as triggers, background functions, HTTP functions-Use cases-Developing and deploying functions-Logging, error reporting, and monitoring Module 11: Managing APIs with Google Cloud Endpoints -Open API deployment configuration-Lab: Deploy an API for your application Module 12: Deploying an Application by Using Google Cloud Build, Google Cloud Container Registry, and Google Cloud Deployment Manager -Creating and storing container images-Repeatable deployments with deployment configuration and templates-Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments Module 13: Execution Environments for Your Application -Considerations for choosing an execution environment for your application or service:-Google Compute Engine-Kubernetes Engine-App Engine flexible environment-Cloud Functions-Cloud Dataflow-Lab: Deploying your application on App Engine flexible environment Module 14: Debugging, Monitoring, and Tuning Performance by Using Google Stackdriver -Stackdriver Debugger-Stackdriver Error Reporting-Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting-Stackdriver Logging-Key concepts related to Stackdriver Trace and Stackdriver Monitoring.-Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance [-]
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2 dager 18 750 kr
04 Feb
12 Apr
This two-day instructor-led class equips students to build highly reliable and efficient solutions on Google Cloud Platform using proven design patterns. [+]
 It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine course and assumes hands-on experience with the technologies covered in either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to design Google Cloud deployments that are highly reliable and secure; and how to operate Google Cloud deployments in a highly available and cost-effective manner.   Course Objectives  This course teaches participants the following skills: Apply a tool set of questions, techniques and design considerations Define application requirements and express them objectively as KPIs, SLO's and SLI's Decompose application requirements to find the right microservice boundaries Leverage Google Cloud developer tools to set up modern, automated deployment pipelines Choose the appropriate Google Cloud Storage services based on application requirements Architect cloud and hybrid networks Implement reliable, scalable, resilient applications balancing key performance metrics with cost Choose the right Google Cloud deployment services for your applications Secure cloud applications, data and infrastructure Monitor service level objectives and costs using Stackdriver tools   Course Outline Module 1: Defining the Service Module 2: Microservice Design and Architecture Module 3: DevOps Automation Module 4: Choosing Storage Solutions Module 5: Google Cloud and Hybrid Network Architecture Module 6: Deploying Applications to Google Cloud Module 7: Designing Reliable Systems Module 8: Security Module 9: Maintenance and Monitoring   [-]
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Nettundervisning 2 dager 18 500 kr
This course begins with an overview of the different cloud computing models and services provided by the major public cloud providers. Several cloud computing concerns li... [+]
Course Description This course then focuses on enterprise application to cloud concerns including planning and executing a migration, building the business case, managing application dependencies, selecting a proof of concept, and serverless/managed services. A series of instructor-led demonstrations and hands-on activities provide students with practical, hands-on experience. Learning Objectives Learn what technologies enable cloud computing Understand the definition and characteristics of cloud computing Compare service models: IaaS, PaaS, SaaS, Serverless Develop the business case for a cloud migration Plan a successful cloud migration Decipher the risks of both development and security with cloud computing Analyze the costs of using cloud computing and an approach to calculating them Objection handling when dealing with projects situations around risk All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Unit 1: Enabling Technologies -Networking-Virtualization-Overview of Virtualization-Hypervisors and Containers-Security and Virtualization-Multi-tenancy Unit 2: Cloud Computing Concepts -Cloud Definition-Characteristics of Clouds-Cloud Service and Deployment Models-Public Cloud Products and Services Unit 3: Cloud Service Models -Comparing Services Offered by Google Cloud Platform (GCP), Amazon Web Services (AWS), and Azure-Compute Services-Storage Services-Kubernetes Services-Serverless and Managed Services-Big Data and Machine Learning Unit 4: Building a Business Case for the Cloud -Economic and Financial-Understand the Cloud Cost Model-Calculating the Cost of a Cloud Solution-Transform Capital Expenditures to Operating Expenditures-Agility-Lower Risk of Adopting and Evaluating New Technology-Reduce Time to Market-Quickly React as Markets and Requirements Change-Risk Mitigation-High Quality Infrastructure-Reduce Downtime-Cloud SLAs-Leveraging Hybrid and Multi-Cloud Solutions-Staff Utilization-Eliminate Mundane Operational Tasks-Harness Monitoring and Logging-Onboarding Applications and Users Unit 5: Migrating to the Public Cloud -Phases in a Successful Migration-Assessment-Proof of Concept-Data Migration-Application Migration-Employ Cloud Native Services-Cloud Native Development-Selecting Workloads-Backup / Disaster Recovery-Packaged Enterprise Software-Custom Applications-Open-Source Applications Unit 6: Security and the Cloud -Cloud-based Security Issues-Shared Responsibility Model-Security Auditing in the Cloud-Compliance with Regulatory Constraints [-]
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1 dag 11 500 kr
11 Feb
11 Mar
This one-day instructor-led class provides an overview of Google Cloud Platform products and services. [+]
This one-day instructor-led class provides an overview of Google Cloud Platform products and services. Through a combination of presentations, demos, and hands-on labs, participants learn the value of Google Cloud Platform and how to incorporate cloud-based solutions into business strategies.    Course Objectives This course teaches participants the following skills: Identify the purpose and value of Google Cloud Platform products and services Interact with Google Cloud Platform services Describe ways in which customers have used Google Cloud Platform Choose among and use application deployment environments on Google Cloud Platform: Google App Engine, Google Container Engine, and Google Compute Engine Choose among and use Google Cloud Platform storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore Make basic use of BigQuery, Google’s managed data warehouse for analytics   Course Outline Module 1: Introducing Google Cloud Platform Module 2: Getting Started with Google Cloud Platform Module 3: Google App Engine and Google Cloud Datastore Module 4: Google Cloud Platform Storage Options Module 5: Google Container Engine Module 6: Google Compute Engine and Networking Module 7: Big Data and Machine Learning   [-]
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Nettundervisning 1 dag 11 000 kr
This one-day instructor-led class provides an overview of Google Cloud Platform products and services. Through a combination of presentations and hands-on labs, participa... [+]
Objectives This course teaches participants the following skills: Identify the purpose and value of each of the Google Cloud Platform products and services Interact with Google Cloud Platform services Describe ways in which customers have used Google Cloud Platform Choose among and use application deployment environments on Google Cloud Platform: Google App Engine, Google Kubernetes Engine, and Google Compute Engine Choose among and use Google Cloud Platform storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore Make basic use of BigQuery, Google’s managed data warehouse for analytics Make basic use of Cloud Deployment Manager, Google’s tool for creating and managing cloud resources through templates Make basic use of Google Stackdriver, Google’s monitoring, logging, and diagnostics system All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Introducing Google Cloud Platform -Explain the advantages of Google Cloud Platform-Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones-Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Module 2: Getting Started with Google Cloud Platform -Identify the purpose of projects on Google Cloud Platform-Understand the purpose of and use cases for Identity and Access Management-List the methods of interacting with Google Cloud Platform-Lab: Getting Started with Google Cloud Platform Module 3: Virtual Machines and Networks in the Cloud -Identify the purpose of and use cases for Google Compute Engine.-Understand the various Google Cloud Platform networking and operational tools and services.-Lab: Compute Engine Module 4: Storage in the Cloud -Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore.-Learn how to choose between the various storage options on Google Cloud Platform.-Lab: Cloud Storage and Cloud SQL Module 5: Containers in the Cloud -Define the concept of a container and identify uses for containers.-Identify the purpose of and use cases for Google Kubernetes Engine and Kubernetes.-Lab: Kubernetes Engine Module 6: Applications in the Cloud -Understand the purpose of and use cases for Google App Engine.-Contrast the App Engine Standard environment with the App Engine Flexible environment.-Understand the purpose of and use cases for Google Cloud Endpoints.-Lab: App Engine Module 7: Developing, Deploying, and Monitoring in the Cloud -Understand options for software developers to host their source code.-Understand the purpose of template-based creation and management of resources.-Understand the purpose of integrated monitoring, alerting, and debugging.-Lab: Deployment Manager and Stackdriver Module 8: Big Data and Machine Learning in the Cloud -Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.-Lab: BigQuery [-]
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Nettundervisning 1 dag 11 000 kr
This course teaches Azure professionals about the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. [+]
The course is designed for Azure system administrators, solutions architects, and SysOps administrators who are familiar with Azure features and setup and want to gain experience configuring Google Cloud products immediately.  This course uses lectures, demos, and hands-on labs to show you the similarities and differences between the two platforms and teach you about some basic tasks on Google Cloud. Objectives This course teaches participants the following skills: Identify Google Cloud counterparts for Azure IaaS, Azure PaaS, Azure SQL, Azure Blob Storage, Azure Application Insights, and Azure Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto-scaling, load balancing, storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Introducing Google Cloud -Explain the advantages of Google Cloud-Define the components of Google’s network infrastructure, including points of presence, data centers, regions, and zones-Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Module 2: Getting Started with Google Cloud -Identify the purpose of projects on Google Cloud-Understand how Azure’s resource hierarchy differs from Google Cloud’s-Understand the purpose of and use cases for Identity and Access Management-Understand how Azure AD differs from Google Cloud IAM-List the methods of interacting with Google Cloud-Launch a solution using Cloud Marketplace Module 3: Virtual Machines in the Cloud -Identify the purpose and use cases for Google Compute Engine-Understand the basics of networking in Google Cloud-Understand how Azure VPC differs from Google VPC-Understand the similarities and differences between Azure VM and Google Compute Engine-Understand how typical approaches to load-balancing in Google Cloud differ from those in AzureDeploy applications using Google Compute Engine Module 4: Storage in the Cloud -Understand the purpose of and use cases for: Cloud Storage, Cloud SQL, Cloud Bigtable and Cloud Datastore-Understand how Azure Blob compares to Cloud Storage-Compare Google Cloud’s managed database services with Azure SQL-Learn how to choose among the various storage options on Google Cloud-Load data from Cloud Storage into BigQuery Module 5: Containers in the Cloud -Define the concept of a container and identify uses for containers-Identify the purpose of and use cases for Google Container Engine and Kubernetes-Understand how Azure Kubernetes Service differs from Google Kubernetes Engine-Provision a Kubernetes cluster using Kubernetes Engine-Deploy and manage Docker containers using kubectl Module 6: Applications in the Cloud -Understand the purpose of and use cases for Google App Engine-Contrast the App Engine Standard environment with the App Engine Flexible environment-Understand how App Engine differs from Azure App Service-Understand the purpose of and use cases for Google Cloud Endpoints Module 7: Developing, Deploying and Monitoring in the Cloud -Understand options for software developers to host their source code-Understand the purpose of template-based creation and management of resources-Understand how Cloud Deployment Manager differs from Azure Resource Manager-Understand the purpose of integrated monitoring, alerting, and debugging-Understand how Google Monitoring differs from Azure Application Insights and Azure Log Analytics-Create a Deployment Manager deployment-Update a Deployment Manager deployment-View the load on a VM instance using Google Monitoring Module 8: Big Data and Machine Learning in the Cloud -Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms-Understand how Google Cloud BigQuery differs from Azure Data Lake-Understand how Google Cloud Pub/Sub differs from Azure Event Hubs and Service Bus-Understand how Google Cloud’s machine-learning APIs differ from Azure’s-Load data into BigQuery from Cloud Storage-Perform queries using BigQuery to gain insight into data Module 9: Summary and Review -Review the products that make up Google Cloud and remember how to choose among them-Understand next steps for training and certification-Understand, at a high level, the process of migrating from Azure to Google Cloud [-]
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Nettundervisning 1 dag 11 000 kr
This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. [+]
Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform. Learning Objectives This course teaches participants the following skills: Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform Employ BigQuery and Cloud Datalab to carry out interactive data analysis Train and use a neural network using TensorFlow Employ ML APIs Choose between different data processing products on the Google Cloud Platform Course Outline Module 1: Introducing Google Cloud Platform -Google Platform Fundamentals Overview-Google Cloud Platform Big Data Products Module 2: Compute and Storage Fundamentals -CPUs on demand (Compute Engine)-A global filesystem (Cloud Storage)-CloudShell-Lab: Set up an Ingest-Transform-Publish data processing pipeline Module 3: Data Analytics on the Cloud -Stepping-stones to the cloud-CloudSQL: your SQL database on the cloud-Lab: Importing data into CloudSQL and running queries-Spark on Dataproc-Lab: Machine Learning Recommendations with Spark on Dataproc Module 4: Scaling Data Analysis -Fast random access-Datalab-BigQuery-Lab: Build machine learning dataset Module 5: Machine Learning -Machine Learning with TensorFlow-Lab: Carry out ML with TensorFlow-Pre-built models for common needs-Lab: Employ ML APIs Module 6: Data Processing Architectures -Message-oriented architectures with Pub/Sub-Creating pipelines with Dataflow-Reference architecture for real-time and batch data processing Module 7: Summary -Why GCP?-Where to go from here-Additional Resources [-]
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Nettundervisning 1 dag 11 000 kr
This course will teach you how to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those wo... [+]
Objectives Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Understand how pod networking works in Google Kubernetes Engine Create and manage Kubernetes Engine clusters using the Google Cloud Console and gcloud/kubectl commands   Course Outline Module 1: Introduction to Google Cloud -Use the Google Cloud Console-Use Cloud Shell-Define Cloud Computing-Identify Google Cloud compute services-Understand Regions and Zones-Understand the Cloud Resource Hierarchy-Administer your Google Cloud Resources Module 2: Containers and Kubernetes in Google Cloud -Create a Container Using Cloud Build-Store a Container in Container Registry-Understand the Relationship Between Kubernetes and Google Kubernetes Engine (GKE)-Understand how to Choose Among Google Cloud Compute Platforms Module 3: Kubernetes Architecture -Understand the Architecture of Kubernetes: Pods, Namespaces-Understand the Control-plane Components of Kubernetes-Create Container Images using Cloud Build-Store Container Images in Container Registry-Create a Kubernetes Engine Cluster Module 4: Introduction to Kubernetes Workloads -The kubectl Command-Introduction to Deployments-Pod Networking-Volumes Overview [-]
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