70 Google Cloud courses

Duration 2 Days 12 CPD hours This course is intended for This class is intended for network engineers and network admins that are either using Google Cloud Platform or are planning to do so. The class is also for individuals that want to be exposed to software-defined networking solutions in the cloud. Overview Configure Google VPC networks, subnets, and routers Control administrative access to VPC objects Control network access to endpoints in VPCsInterconnect networks among GCP projects Interconnect networks among GCP VPC networks and on-premises or other-cloud networks Choose among GCP load balancer and proxy options and configure them Use Cloud CDN to reduce latency and save money Optimize network spend using Network TiersConfigure Cloud NAT or Private Google Access to provide instances without public IP addresses access to other services Deploy networks declaratively using Cloud Deployment Manager or Terraform Design networks to meet common customer requirements Configure monitoring and logging to troubleshoot networks problems Learn about the broad variety of networking options on Google Cloud. This course uses lectures, demos, and hands-on labs to help you explore and deploy Google Cloud networking technologies, including Virtual Private Cloud (VPC) networks, subnets, and firewalls; interconnection among networks; load balancing; Cloud DNS; Cloud CDN; and Cloud NAT. You'll also learn about common network design patterns and automated deployment using Cloud Deployment Manager or Terraform. GOOGLE CLOUD VPC NETWORKING FUNDAMENTALS * Recall that networks belong to projects. * Explain the differences among default, auto, and custom networks. * Create networks and subnets. * Explain how IPv4 addresses are assigned to Compute Engine instances. * Publish domain names using Google Cloud DNS. * Create Compute Engine instances with IP aliases. * Create Compute Engine instances with multiple virtual network. CONTROLLING ACCESS TO VPC NETWORKS * Outline how IAM policies affect VPC networks. * Control access to network resources using service accounts. * Control access to Compute Engine instances with tag-based firewall rules. SHARING NETWORKS ACROSS PROJECTS * Outline the overall workflow for configuring Shared VPC. * Differentiate between the IAM roles that allow network resources to be managed. * Configure peering between unrelated VPC Networks. * Recall when to use Shared VPC and when to use VPC Network Peering. LOAD BALANCING * Recall the various load balancing services. * Configure Layer 7 HTTP(S) load balancing. * Whitelist and blacklist IP traffic with Cloud Armor. * Cache content with Cloud CDN. * Explain Layer 4 TCP or SSL proxy load balancing. * Explain regional network load balancing. * Configure internal load balancing. * Recall the choices for enabling IPv6 Internet connectivity for Google Cloud load balancers. * Determine which Google Cloud load balancer to use when. HYBRID CONNECTIVITY * Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud. * Explain Dedicated Interconnect and Partner Interconnect. * Describe the workflow for configuring a Dedicated Interconnect. * Build a connection over a VPN with Cloud Router. * Determine which Google Cloud interconnect service to use when. * Explain Direct Peering and Partner Peering. * Determine which Google Cloud peering service to use when. NETWORKING PRICING AND BILLING * Recognize how networking features are charged for. * Use Network Service Tiers to optimize spend. * Determine which Network Service Tier to use when. * Recall that labels can be used to understand networking spend. NETWORK DESIGN AND DEPLOYMENT * Explain common network design patterns. * Configure Private Google Access to allow access to certain Google Cloud services from VM instances with only internal IP addresses. * Configure Cloud NAT to provide your instances without public IP addresses access to the internet. * Automate the deployment of networks using Deployment Manager or Terraform. * Launch networking solutions using Cloud Marketplace. NETWORK MONITORING AND TROUBLESHOOTING * Configure uptime checks, alerting policies and charts for your network services. * Use VPC Flow Logs to log and analyze network traffic behavior.

Duration 1 Days 6 CPD hours This course is intended for Individuals planning to deploy applications and create application environments on Google Cloud. Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview Identify the purpose and value of Google Cloud products and services. Interact with Google Cloud services. Describe ways in which customers have used Google Cloud. Choose among and use application deployment environments on Google Cloud: App Engine, Google Kubernetes Engine, and Compute Engine. Choose among and use Google Cloud storage options: Cloud Storage, Cloud SQL, Cloud Bigtable, and Firestore. Make basic use of BigQuery, Google's managed data warehouse for analytics. This course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies. 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). 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. GOOGLE COMPUTE ENGINE AND NETWORKING * Identify the purpose of and use cases for Google Compute Engine. * Understand the basics of networking in Google Cloud Platform. * Lab: Deploying Applications Using Google Compute Engine. GOOGLE CLOUD PLATFORM STORAGE OPTIONS * Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable. * Learn how to choose between the various storage options on Google Cloud Platform. * Lab: Integrating Applications with Google Cloud Storage. GOOGLE CONTAINER ENGINE * Define the concept of a container and identify uses for containers. * Identify the purpose of and use cases for Google Container Engine and Kubernetes. * Introduction to Hybrid and Multi-Cloud computing (Anthos). * Lab: Deploying Applications Using Google Container Engine. GOOGLE APP ENGINE AND GOOGLE CLOUD DATASTORE * Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore. * Contrast the App Engine Standard environment with the App Engine Flexible environment. * Understand the purpose of and use cases for Google Cloud Endpoints. * Lab: Deploying Applications Using App Engine and Cloud Datastore. DEPLOYMENT AND MONITORING * Understand the purpose of template-based creation and management of resources. * Understand the purpose of integrated monitoring, alerting, and debugging. * Lab: Getting Started with Stackdriver and Deployment Manager. BIG DATA AND MACHINE LEARNING * Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. * Lab: Getting Started with BigQuery. SUMMARY AND REVIEW * Summary and Review. * What's Next?.

Duration 3 Days 18 CPD hours This course is intended for Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform Overview 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 flexible environment. Learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. This course uses lectures, demos, and hands-on labs to show you how to use Google Cloud services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. 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. 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, Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials. OVERVIEW OF DATA STORAGE OPTIONS * Overview of options to store application data. * Use cases for Google Cloud Storage, Cloud Firestore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner. BEST PRACTICES FOR USING CLOUD FIRESTORE * Best practices related to using Cloud Firestore in Datastore mode for:Queries, Built-in and composite indexes, Inserting and deleting data (batch operations),Transactions,Error handling. * Bulk-loading data into Cloud Firestore by using Google Cloud Dataflow. * Lab: Store application data in Cloud Datastore. PERFORMING OPERATIONS ON CLOUD STORAGE * Operations that can be performed on buckets and objects. * Consistency model. * Error handling. 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. 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. USING 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. ADDING INTELLIGENCE TO YOUR APPLICATION * Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API. 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. MANAGING APIS WITH CLOUD ENDPOINTS * Open API deployment configuration. * Lab: Deploy an API for your application. DEPLOYING APPLICATIONS * 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. EXECUTION ENVIRONMENTS FOR YOUR APPLICATION * Considerations for choosing an execution environment for your application or service:Google Compute Engine (GCE),Google Kubernetes Engine (GKE), App Engine flexible environment, Cloud Functions, Cloud Dataflow, Cloud Run. * Lab: Deploying your application on App Engine flexible environment. DEBUGGING, MONITORING, AND TUNING PERFORMANCE * Application Performance Management Tools. * 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.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Individuals planning to deploy applications and create application environments on Google Cloud Platform Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs. Overview 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 This 1-day instructor led course introduces Azure professionals to the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for Azure system administrators, Solution Architects and SysOps Administrators familiar with Azure features and setup; and want to gain experience configuring Google Cloud products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly. 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). * 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. 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 Azure. * Deploy applications using Google Compute Engine 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 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 from Google Kubernetes Engine. * Provision a Kubernetes cluster using Kubernetes Engine. * Deploy and manage Docker containers using kubectl 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. * 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 Google 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. 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 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.

Duration 3 Days 18 CPD hours This course is intended for This class is intended for the following job roles: [Cloud] information security analysts, architects, and engineers Information security/cybersecurity specialists Cloud infrastructure architects Additionally, the course is intended for Google and partner field personnel who work with customers in those job roles. The course should also be useful to developers of cloud applications Overview This course teaches participants the following skills: Understanding the Google approach to security Managing administrative identities using Cloud Identity. Implementing least privilege administrative access using Google Cloud Resource Manager, Cloud IAM. Implementing IP traffic controls using VPC firewalls and Cloud Armor Implementing Identity Aware Proxy Analyzing changes to the configuration or metadata of resources with GCP audit logs Scanning for and redact sensitive data with the Data Loss Prevention API Scanning a GCP deployment with Forseti Remediating important types of vulnerabilities, especially in public access to data and VMs This course gives participants broad study of security controls and techniques on Google Cloud Platform. Through lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution. Participants also learn mitigation techniques for attacks at many points in a Google Cloud-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use. FOUNDATIONS OF GCP SECURITY * Google Cloud's approach to security * The shared security responsibility model * Threats mitigated by Google and by GCP * Access Transparency CLOUD IDENTITY * Cloud Identity * Syncing with Microsoft Active Directory * Choosing between Google authentication and SAML-based SSO * GCP best practices IDENTITY AND ACCESS MANAGEMENT * GCP Resource Manager: projects, folders, and organizations * GCP IAM roles, including custom roles * GCP IAM policies, including organization policies * GCP IAM best practices CONFIGURING GOOGLE VIRTUAL PRIVATE CLOUD FOR ISOLATION AND SECURITY * Configuring VPC firewalls (both ingress and egress rules) * Load balancing and SSL policies * Private Google API access * SSL proxy use * Best practices for structuring VPC networks * Best security practices for VPNs * Security considerations for interconnect and peering options * Available security products from partners MONITORING, LOGGING, AUDITING, AND SCANNING * Stackdriver monitoring and logging * VPC flow logs * Cloud audit logging * Deploying and Using Forseti SECURING COMPUTE ENGINE: TECHNIQUES AND BEST PRACTICES * Compute Engine service accounts, default and customer-defined * IAM roles for VMs * API scopes for VMs * Managing SSH keys for Linux VMs * Managing RDP logins for Windows VMs * Organization policy controls: trusted images, public IP address, disabling serial port * Encrypting VM images with customer-managed encryption keys and with customer-supplied encryption keys * Finding and remediating public access to VMs * VM best practices * Encrypting VM disks with customer-supplied encryption keys SECURING CLOUD DATA: TECHNIQUES AND BEST PRACTICES * Cloud Storage and IAM permissions * Cloud Storage and ACLs * Auditing cloud data, including finding and remediating publicly accessible data * Signed Cloud Storage URLs * Signed policy documents * Encrypting Cloud Storage objects with customer-managed encryption keys and with customer-supplied encryption keys * Best practices, including deleting archived versions of objects after key rotation * BigQuery authorized views * BigQuery IAM roles * Best practices, including preferring IAM permissions over ACLs PROTECTING AGAINST DISTRIBUTED DENIAL OF SERVICE ATTACKS: TECHNIQUES AND BEST PRACTICES * How DDoS attacks work * Mitigations: GCLB, Cloud CDN, autoscaling, VPC ingress and egress firewalls, Cloud Armor * Types of complementary partner products APPLICATION SECURITY: TECHNIQUES AND BEST PRACTICES * Types of application security vulnerabilities * DoS protections in App Engine and Cloud Functions * Cloud Security Scanner * Threat: Identity and Oauth phishing * Identity Aware Proxy CONTENT-RELATED VULNERABILITIES: TECHNIQUES AND BEST PRACTICES * Threat: Ransomware * Mitigations: Backups, IAM, Data Loss Prevention API * Threats: Data misuse, privacy violations, sensitive/restricted/unacceptable content * Mitigations: Classifying content using Cloud ML APIs; scanning and redacting data using Data Loss Prevention API ADDITIONAL COURSE DETAILS: Nexus Humans Security in Google Cloud training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Security in Google Cloud course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Individuals planning to deploy applications and create application environments on Google Cloud Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview This course teaches participants the following skills: Identify Google Cloud counterparts for AWS IaaS, AWS PaaS, AWS SQL, AWS Blob Storage, AWS Application Insights, and AWS 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 This course with labs introduces AWS professionals to the core capabilities of Google Cloud Platform (GCP) in the four technology pillars: networking, compute, storage, and database. It is designed for AWS Solution Architects and SysOps Administrators familiar with AWS features and setup and want to gain experience configuring GCP products immediately. With presentations, demos, and hands-on labs, participants will get details of similarities, differences, and initial how-tos quickly. 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) GETTING STARTED WITH GOOGLE CLOUD * Identify the purpose of projects on Google Cloud Platform. * Understand how AWS?s resource hierarchy differs from Google Cloud?s. * Understand the purpose of and use cases for Identity and Access Management. * Understand how AWS IAM differs from Google Cloud IAM. * List the methods of interacting with Google Cloud Platform. * Launch a solution using Cloud Marketplace. VIRTUAL MACHINES IN THE CLOUD * Identify the purpose and use cases for Google Compute Engine. * Understand the basics of networking in Google Cloud Platform. * Understand how Amazon VPC differs from Google VPC. * Understand the similarities and differences between Amazon EC2 and Google Compute Engine. * Understand how typical approaches to load-balancing in Google Cloud differ from those in AWS. * Deploy applications using Google Compute Engine. STORAGE IN THE CLOUD * Understand the purpose of and use cases for: Cloud Storage,Cloud SQL, Cloud Bigtable and Cloud Datastore. * Understand how Amazon S3 and Amazon Glacier compare to Cloud Storage. * Compare Google Cloud?s managed database services with Amazon RDS and Amazon Aurora. * Learn how to choose among the various storage options on Google Cloud Platform. * Load data from Cloud Storage into BigQuery. Perform a query on the data in BigQuery. * 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 Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) differ from GKE. * Provision a Kubernetes cluster using Kubernetes Engine. * Deploy and manage Docker containers using kubectl 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 Amazon Elastic Beanstalk. * Understand the purpose of and use cases for Google Cloud Endpoints. * 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 AWS CloudFormation. * Understand the purpose of integrated monitoring, alerting, and debugging. * Understand how Google Monitoring differs from Amazon CloudWatch and AWS CloudTrail. * Create a Deployment Manager deployment. * Update a Deployment Manager deployment. * View the load on a VM instance using Google Monitoring. 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 AWS Data Lake. * Understand how Google Cloud Pub/Sub differs from AWS Event Hubs and Service Bus. * Understand how Google Cloud?s machine-learning APIs differ from AWS's. * Load data into BigQuery from Cloud Storage. * Perform queries using BigQuery to gain insight into data. 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 AWS to Google Cloud. ADDITIONAL COURSE DETAILS: Nexus Humans Google Cloud Fundamentals for AWS Professionals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Google Cloud Fundamentals for AWS Professionals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for Cloud Solutions Architects  DevOps Engineers  Individuals using Google Cloud Platform who deploy applications, monitor operations, and manage enterprise solutions Overview At course completion, you will have attained knowledge of: Fundamentals of Google Cloud Platform (GCP) Google Cloud Storage Google Compute Engine Google Cloud SQL Load Balancing (LB) Google Cloud Monitoring Auto-Scaling Virtual Private Cloud (VPC) Network Cloud Identity and Access Management (IAM) Cloud CDN and DNS Cloud VPN Google Cloud Deployment Manager Google Container Engine Cloud Run Cloud Bigtable Cloud Datastore Cloud BigQuery Cloud DataFlow Cloud DataProc Cloud Pub/Sub In this course you will attain a deep knowledge of Google Cloud Platform infrastructure and design patterns on developing applications on GCP. This course will prepare you for the Google Cloud Architect Associate Certification Exam. FUNDAMENTALS OF GOOGLE CLOUD PLATFORM (GCP) * Overview * Regions and Zones * Review of major GCP services * GOOGLE CLOUD STORAGE * Fundamental APIs * Consistency * Cloud Storage Namespace * Buckets and Objects * Bucket and Object Naming Guidelines * Encryption * Object Versioning * Object Lifecycle Management * Access Control * Access Control Lists * Signed URL * Multipart upload * Resumable upload * Understanding Pricing for Cloud Storage * Offline Media Import/Export * Architecture case study of common Use Cases of Google Cloud Storage * Hands-on: Cloud Storage Lab; Creating Buckets, objects, and managing access control * GOOGLE COMPUTE ENGINE * Compute Engine Architecture * VM Instances types * Persistence Disks * Images * Generating Custom Images * IP Addresses * Static IPs * Ephemeral * Access Control Options * IAM * Service Account * Monitoring Instances with Google Cloud Monitoring * Compute Engine Networks and Firewalls * Hands-on: Hosting an Application on Compute Engine * GOOGLE CLOUD SQL * Core advantages of Cloud SQL * Cloud SQL database instance types * Access Control * High availability options * Failover * Read replica * Backup options * On Demand * Automated * Understanding Pricing of Cloud SQL * LOAD BALANCING (LB) * Fundamentals of a Load Balancer * Network Load balancing * HTTPS Load balancing * Cross region Load balancing * Content Load balancing * Target proxies * SSL Load Balancing * Internal Load Balancing * Network Load Balancing * Understanding Pricing for Load Balancer * GOOGLE CLOUD MONITORING * Architecture of Cloud Monitoring * Supported metrics * Stackdriver Monitoring APIs * AUTO-SCALING * Overview of Autoscaling * Auto-scaling Fundamentals * Instance groups * Templates * Policies * Decisions * Hands-on: Deploying a scale application on GCP using Autoscaling, Compute Engine, Cloud SQL, Load Balancers. * VIRTUAL PRIVATE CLOUD (VPC) NETWORK * Salient features of Virtual Private Cloud (VPC) Network Infrastructure * Virtual Private Cloud (VPC) Networking Fundamentals * Subnetworks * Firewall * Internal DNS * Network Routes * Hands-on: Hosting Secure Applications in Google Cloud VPC Networks * CLOUD IDENTITY AND ACCESS MANAGEMENT (IAM) * Introduction * User and Service Accounts * IAM Roles * Policy * Hands-on: Managing Users, Policies and Granting Roles using Service Accounts * CLOUD CDN AND DNS * What is CDN * Google Cloud CDN * Cloud CDN Concepts * Some of the Cloud CDN Edge locations * Cloud DNS * Cloud DNS Terminologies * Supported Record Types * Hands-on: Moving an Existing Domain Name to Cloud DNS * CLOUD VPN * Cloud VPN overview * Types of Cloud VPN * Specifications * Maintenance and Availability * Google Cloud Deployment Manager * Deployment Manager * Deployment Manager Fundamentals * Runtime Configurator * Quotas * Hands-on: Generating and Creating Cloud Deployment Manager Template * GOOGLE CONTAINER ENGINE * Google Container Engine Overview * Docker Overview * Kubernetes Terminologies * Replication Controller * Deployment * Price and Quotas * Hands-on: Deploying WordPress Cluster using Container Engine * CLOUD RUN * Overview of Cloud Run * Deploy a Prebuilt Sample container * CLOUD BIGTABLE * Overview of Cloud Bigtable * Access Control * Performance * Locations * Cloud Datastore * Overview of Cloud Datastore * Limits * Storage Size * Multitenancy * Benefits of Multitenancy * Encryption * Locations * CLOUD BIGQUERY * BigQuery Overview * Interacting with BigQuery * Datasets, Tables, and Views * Partitioned Tables * Query Plan Explanation * Hands-on: Getting Started with BigQuery * CLOUD DATAFLOW * Overview * Programming Model * DataFlow SDK 1.x for java * Cloud Dataflow SDK 2.x * Security and Permissions * Advanced Access Control * CLOUD DATAPROC * Overview * Clusters * Versioning * CLOUD PUB/SUB * Overview of Cloud Pub/Sub * Pub/Sub Concepts and Message Flow * Data Model * Cleanup of All Services * Hands-on: Cloud Pub/Sub Lab with Background Cloud Function * ADDITIONAL COURSE DETAILS: Nexus Humans Google Cloud Engineer Associate Certification Bootcamp training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Google Cloud Engineer Associate Certification Bootcamp course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for Cloud Solutions Architects, Site Reliability Engineers, Systems Operations professionals, DevOps Engineers, IT managers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform. Overview 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 This course features a combination of lectures, design activities, and hands-on labs to show you how to use proven design patterns on Google Cloud to build highly reliable and efficient solutions and operate deployments that are highly available and cost-effective. This course was created for those who have already completed the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine course. DEFINING THE SERVICE * Describe users in terms of roles and personas. * Write qualitative requirements with user stories. * Write quantitative requirements using key performance indicators (KPIs). * Evaluate KPIs using SLOs and SLIs. * Determine the quality of application requirements using SMART criteria. MICROSERVICE DESIGN AND ARCHITECTURE * Decompose monolithic applications into microservices. * Recognize appropriate microservice boundaries. * Architect stateful and stateless services to optimize scalability and reliability. * Implement services using 12-factor best practices. * Build loosely coupled services by implementing a well-designed REST architecture. * Design consistent, standard RESTful service APIs. DEVOPS AUTOMATION * Automate service deployment using CI/CD pipelines. * Leverage Cloud Source Repositories for source and version control. * Automate builds with Cloud Build and build triggers. * Manage container images with Google Container Registry. * Create infrastructure with code using Deployment Manager and Terraform. CHOOSING STORAGE SOLUTIONS * Choose the appropriate Google Cloud data storage service based on use case, durability, availability, scalability and cost. * Store binary data with Cloud Storage. * Store relational data using Cloud SQL and Spanner. * Store NoSQL data using Firestore and Cloud Bigtable. * Cache data for fast access using Memorystore. * Build a data warehouse using BigQuery. GOOGLE CLOUD AND HYBRID NETWORK ARCHITECTURE * Design VPC networks to optimize for cost, security, and performance. * Configure global and regional load balancers to provide access to services. * Leverage Cloud CDN to provide lower latency and decrease network egress. * Evaluate network architecture using the Cloud Network Intelligence Center. * Connect networks using peering and VPNs. * Create hybrid networks between Google Cloud and on-premises data centers using Cloud Interconnect. DEPLOYING APPLICATIONS TO GOOGLE CLOUD * Choose the appropriate Google Cloud deployment service for your applications. * Configure scalable, resilient infrastructure using Instance Templates and Groups. * Orchestrate microservice deployments using Kubernetes and GKE. * Leverage App Engine for a completely automated platform as a service (PaaS). * Create serverless applications using Cloud Functions. DESIGNING RELIABLE SYSTEMS * Design services to meet requirements for availability, durability, and scalability. * Implement fault-tolerant systems by avoiding single points of failure, correlated failures, and cascading failures. * Avoid overload failures with the circuit breaker and truncated exponential backoff design patterns. * Design resilient data storage with lazy deletion. * Analyze disaster scenarios and plan for disaster recovery using cost/risk analysis. SECURITY * Design secure systems using best practices like separation of concerns, principle of least privilege, and regular audits. * Leverage Cloud Security Command Center to help identify vulnerabilities. * Simplify cloud governance using organizational policies and folders. * Secure people using IAM roles, Identity-Aware Proxy, and Identity Platform. * Manage the access and authorization of resources by machines and processes using service accounts. * Secure networks with private IPs, firewalls, and Private Google Access. * Mitigate DDoS attacks by leveraging Cloud DNS and Cloud Armor. MAINTENANCE AND MONITORING * Manage new service versions using rolling updates, blue/green deployments, and canary releases. * Forecast, monitor, and optimize service cost using the Google Cloud pricing calculator and billing reports and by analyzing billing data. * Observe whether your services are meeting their SLOs using Cloud Monitoring and Dashboards. * Use Uptime Checks to determine service availability. * Respond to service outages using Cloud Monitoring Alerts. ADDITIONAL COURSE DETAILS: Nexus Humans Architecting with Google Cloud: Design and Process training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Architecting with Google Cloud: Design and Process course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. INTRODUCTION TO DATA ENGINEERING * Explore the role of a data engineer. * Analyze data engineering challenges. * Intro to BigQuery. * Data Lakes and Data Warehouses. * Demo: Federated Queries with BigQuery. * Transactional Databases vs Data Warehouses. * Website Demo: Finding PII in your dataset with DLP API. * Partner effectively with other data teams. * Manage data access and governance. * Build production-ready pipelines. * Review GCP customer case study. * Lab: Analyzing Data with BigQuery. BUILDING A DATA LAKE * Introduction to Data Lakes. * Data Storage and ETL options on GCP. * Building a Data Lake using Cloud Storage. * Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. * Securing Cloud Storage. * Storing All Sorts of Data Types. * Video Demo: Running federated queries on Parquet and ORC files in BigQuery. * Cloud SQL as a relational Data Lake. * Lab: Loading Taxi Data into Cloud SQL. BUILDING A DATA WAREHOUSE * The modern data warehouse. * Intro to BigQuery. * Demo: Query TB+ of data in seconds. * Getting Started. * Loading Data. * Video Demo: Querying Cloud SQL from BigQuery. * Lab: Loading Data into BigQuery. * Exploring Schemas. * Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. * Schema Design. * Nested and Repeated Fields. * Demo: Nested and repeated fields in BigQuery. * Lab: Working with JSON and Array data in BigQuery. * Optimizing with Partitioning and Clustering. * Demo: Partitioned and Clustered Tables in BigQuery. * Preview: Transforming Batch and Streaming Data. INTRODUCTION TO BUILDING BATCH DATA PIPELINES * EL, ELT, ETL. * Quality considerations. * How to carry out operations in BigQuery. * Demo: ELT to improve data quality in BigQuery. * Shortcomings. * ETL to solve data quality issues. EXECUTING SPARK ON CLOUD DATAPROC * The Hadoop ecosystem. * Running Hadoop on Cloud Dataproc. * GCS instead of HDFS. * Optimizing Dataproc. * Lab: Running Apache Spark jobs on Cloud Dataproc. SERVERLESS DATA PROCESSING WITH CLOUD DATAFLOW * Cloud Dataflow. * Why customers value Dataflow. * Dataflow Pipelines. * Lab: A Simple Dataflow Pipeline (Python/Java). * Lab: MapReduce in Dataflow (Python/Java). * Lab: Side Inputs (Python/Java). * Dataflow Templates. * Dataflow SQL. MANAGE DATA PIPELINES WITH CLOUD DATA FUSION AND CLOUD COMPOSER * Building Batch Data Pipelines visually with Cloud Data Fusion. * Components. * UI Overview. * Building a Pipeline. * Exploring Data using Wrangler. * Lab: Building and executing a pipeline graph in Cloud Data Fusion. * Orchestrating work between GCP services with Cloud Composer. * Apache Airflow Environment. * DAGs and Operators. * Workflow Scheduling. * Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. * Monitoring and Logging. * Lab: An Introduction to Cloud Composer. INTRODUCTION TO PROCESSING STREAMING DATA * Processing Streaming Data. SERVERLESS MESSAGING WITH CLOUD PUB/SUB * Cloud Pub/Sub. * Lab: Publish Streaming Data into Pub/Sub. CLOUD DATAFLOW STREAMING FEATURES * Cloud Dataflow Streaming Features. * Lab: Streaming Data Pipelines. HIGH-THROUGHPUT BIGQUERY AND BIGTABLE STREAMING FEATURES * BigQuery Streaming Features. * Lab: Streaming Analytics and Dashboards. * Cloud Bigtable. * Lab: Streaming Data Pipelines into Bigtable. ADVANCED BIGQUERY FUNCTIONALITY AND PERFORMANCE * Analytic Window Functions. * Using With Clauses. * GIS Functions. * Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. * Performance Considerations. * Lab: Optimizing your BigQuery Queries for Performance. * Optional Lab: Creating Date-Partitioned Tables in BigQuery. INTRODUCTION TO ANALYTICS AND AI * What is AI?. * From Ad-hoc Data Analysis to Data Driven Decisions. * Options for ML models on GCP. PREBUILT ML MODEL APIS FOR UNSTRUCTURED DATA * Unstructured Data is Hard. * ML APIs for Enriching Data. * Lab: Using the Natural Language API to Classify Unstructured Text. BIG DATA ANALYTICS WITH CLOUD AI PLATFORM NOTEBOOKS * What's a Notebook. * BigQuery Magic and Ties to Pandas. * Lab: BigQuery in Jupyter Labs on AI Platform. PRODUCTION ML PIPELINES WITH KUBEFLOW * Ways to do ML on GCP. * Kubeflow. * AI Hub. * Lab: Running AI models on Kubeflow. CUSTOM MODEL BUILDING WITH SQL IN BIGQUERY ML * BigQuery ML for Quick Model Building. * Demo: Train a model with BigQuery ML to predict NYC taxi fares. * Supported Models. * Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. * Lab Option 2: Movie Recommendations in BigQuery ML. CUSTOM MODEL BUILDING WITH CLOUD AUTOML * Why Auto ML? * Auto ML Vision. * Auto ML NLP. * Auto ML Tables.

Duration 3 Days 18 CPD hours This course is intended for This class is intended for the following customer job roles: Cloud architects, administrators, and SysOps personnel Cloud developers and DevOps personnel Overview This course teaches participants the following skills: Plan and implement a well-architected logging and monitoring infrastructure Define Service Level Indicators (SLIs) and Service Level Objectives (SLOs) Create effective monitoring dashboards and alerts Monitor, troubleshoot, and improve Google Cloud infrastructure Analyze and export Google Cloud audit logs Find production code defects, identify bottlenecks, and improve performance Optimize monitoring costs This course teaches you techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud. Guided by the principles of Site Reliability Engineering (SRE), and using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage. INTRODUCTION TO GOOGLE CLOUD MONITORING TOOLS * Understand the purpose and capabilities of Google Cloud operations-focused components: Logging, Monitoring, Error Reporting, and Service Monitoring * Understand the purpose and capabilities of Google Cloud application performance management focused components: Debugger, Trace, and Profiler AVOIDING CUSTOMER PAIN * Construct a monitoring base on the four golden signals: latency, traffic, errors, and saturation * Measure customer pain with SLIs * Define critical performance measures * Create and use SLOs and SLAs * Achieve developer and operation harmony with error budgets ALERTING POLICIES * Develop alerting strategies * Define alerting policies * Add notification channels * Identify types of alerts and common uses for each * Construct and alert on resource groups * Manage alerting policies programmatically MONITORING CRITICAL SYSTEMS * Choose best practice monitoring project architectures * Differentiate Cloud IAM roles for monitoring * Use the default dashboards appropriately * Build custom dashboards to show resource consumption and application load * Define uptime checks to track aliveness and latency CONFIGURING GOOGLE CLOUD SERVICES FOR OBSERVABILITY * Integrate logging and monitoring agents into Compute Engine VMs and images * Enable and utilize Kubernetes Monitoring * Extend and clarify Kubernetes monitoring with Prometheus * Expose custom metrics through code, and with the help of OpenCensus ADVANCED LOGGING AND ANALYSIS * Identify and choose among resource tagging approaches * Define log sinks (inclusion filters) and exclusion filters * Create metrics based on logs * Define custom metrics * Link application errors to Logging using Error Reporting * Export logs to BigQuery MONITORING NETWORK SECURITY AND AUDIT LOGS * Collect and analyze VPC Flow logs and Firewall Rules logs * Enable and monitor Packet Mirroring * Explain the capabilities of Network Intelligence Center * Use Admin Activity audit logs to track changes to the configuration or metadata of resources * Use Data Access audit logs to track accesses or changes to user-provided resource data * Use System Event audit logs to track GCP administrative actions MANAGING INCIDENTS * Define incident management roles and communication channels * Mitigate incident impact * Troubleshoot root causes * Resolve incidents * Document incidents in a post-mortem process INVESTIGATING APPLICATION PERFORMANCE ISSUES * Debug production code to correct code defects * Trace latency through layers of service interaction to eliminate performance bottlenecks * Profile and identify resource-intensive functions in an application OPTIMIZING THE COSTS OF MONITORING * Analyze resource utilization cust for monitoring related components within Google Cloud * Implement best practices for controlling the cost of monitoring within Google Cloud