76 Google Cloud courses delivered Online

Gain the solid skills and knowledge to kickstart a successful career and learn from the experts with this

Duration 3 Days 18 CPD hours This course is intended for This class is intended for the following participants: Cloud architects, administrators, and SysOps/DevOps personnel Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform. Overview This course teaches participants the following skills: Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Platform Understand how pod networking works in Kubernetes Engine Create and manage Kubernetes Engine clusters using the GCP Console and gcloud/ kubectl commands Launch, roll back and expose jobs in Kubernetes Manage access control using Kubernetes RBAC and Google Cloud IAM Managing pod security policies and network policies Using Secrets and ConfigMaps to isolate security credentials and configuration artifacts Understand GCP choices for managed storage services Monitor applications running in Kubernetes Engine This class introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring. Introduction to Google Cloud Platform Use the Google Cloud Platform Console Use Cloud Shell Define cloud computing Identify GCPs compute services Understand regions and zones Understand the cloud resource hierarchy Administer your GCP resources Containers and Kubernetes in GCP 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 GCP compute platforms Kubernetes Architecture Understand the architecture of Kubernetes: pods, namespaces Understand the control-plane components of Kubernetes Create container images using Google Cloud Build Store container images in Google Container Registry Create a Kubernetes Engine cluster Kubernetes Operations Work with the kubectl command Inspect the cluster and Pods View a Pods console output Sign in to a Pod interactively Deployments, Jobs, and Scaling Create and use Deployments Create and run Jobs and CronJobs Scale clusters manually and automatically Configure Node and Pod affinity Get software into your cluster with Helm charts and Kubernetes Marketplace GKE Networking Create Services to expose applications that are running within Pods Use load balancers to expose Services to external clients Create Ingress resources for HTTP(S) load balancing Leverage container-native load balancing to improve Pod load balancing Define Kubernetes network policies to allow and block traffic to pods Persistent Data and Storage Use Secrets to isolate security credentials Use ConfigMaps to isolate configuration artifacts Push out and roll back updates to Secrets and ConfigMaps Configure Persistent Storage Volumes for Kubernetes Pods Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts Access Control and Security in Kubernetes and Kubernetes Engine Understand Kubernetes authentication and authorization Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources Define Kubernetes pod security policies Understand the structure of GCP IAM Define IAM roles and policies for Kubernetes Engine cluster administration Logging and Monitoring Use Stackdriver to monitor and manage availability and performance Locate and inspect Kubernetes logs Create probes for wellness checks on live applications Using GCP Managed Storage Services from Kubernetes Applications Understand pros and cons for using a managed storage service versus self-managed containerized storage Enable applications running in GKE to access GCP storage services Understand use cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and Bigquery from within a Kubernetes application

Are you interested in learning and deploying applications at scale using Google Cloud platform? Do you lack hands-on exposure when it comes to deploying applications and seeing them in action? Then this course is for you. You will also learn microservices and event-driven architectures with real-world use case implementations.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Application developers, Cloud Solutions Architects, 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 At the end of the course, students will be able to: Understand container basics. Containerize an existing application. Understand Kubernetes concepts and principles. Deploy applications to Kubernetes using the CLI. Set up a continuous delivery pipeline using Jenkins Learn to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic. Students will also learn how to continuously deploy new code in a Kubernetes cluster to provide application updates. Introduction to Containers and Docker Acquaint yourself with containers, Docker, and the Google Container Registry. Create a container. Package a container using Docker. Store a container image in Google Container Registry. Launch a Docker container. Kubernetes Basics Deploy an application with microservices in a Kubernetes cluster. Provision a complete Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl. Break an application into microservices using Kubernetes? Deployments and Services. Deploying to Kubernetes Create and manage Kubernetes deployments. Create a Kubernetes deployment. Trigger, pause, resume, and rollback updates. Understand and build canary deployments. Continuous Deployment with Jenkins Build a continuous delivery pipeline. Provision Jenkins in your Kubernetes cluster. Create a Jenkins pipeline. Implement a canary deployment using Jenkins. Additional course details: Nexus Humans Getting Started with Google Kubernetes Engine 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 Getting Started with Google Kubernetes Engine 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 3 Days 18 CPD hours This course is intended for Cloud Solutions Architects, DevOps Engineers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with a focus on Google Compute Engine. Overview Configure VPC networks and virtual machines Administer Identity and Access Management for resources Implement data storage services in GCP Manage and examine billing of GCP resources Monitor resources using Stackdriver services Connect your infrastructure to GCP Configure load balancers and autoscaling for VM instances Automate the deployment of GCP infrastructure services Leverage managed services in GCP This class introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform, with a focus on Compute Engine. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems, and application services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring. Introduction to Google Cloud Platform List the different ways of interacting with GCP Use the GCP Console and Cloud Shell Create Cloud Storage buckets Use the GCP Marketplace to deploy solutions Virtual Networks List the VPC objects in GCP Differentiate between the different types of VPC networks Implement VPC networks and firewall rules Design a maintenance server Virtual Machines Recall the CPU and memory options for virtual machines Describe the disk options for virtual machines Explain VM pricing and discounts Use Compute Engine to create and customize VM instances Cloud IAM Describe the Cloud IAM resource hierarchy Explain the different types of IAM roles Recall the different types of IAM members Implement access control for resources using Cloud IAM Storage and Database Services Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable Choose a data storage service based on your requirements Implement data storage services Resource Management Describe the cloud resource manager hierarchy Recognize how quotas protect GCP customers Use labels to organize resources Explain the behavior of budget alerts in GCP Examine billing data with BigQuery Resource Monitoring Describe the Stackdriver services for monitoring, logging, error reporting, tracing, and debugging Create charts, alerts, and uptime checks for resources with Stackdriver Monitoring Use Stackdriver Debugger to identify and fix errors Interconnecting Networks Recall the GCP interconnect and peering services available to connect your infrastructure to GCP Determine which GCP interconnect or peering service to use in specific circumstances Create and configure VPN gateways Recall when to use Shared VPC and when to use VPC Network Peering Load Balancing and Autoscaling Recall the various load balancing services Determine which GCP load balancer to use in specific circumstances Describe autoscaling behavior Configure load balancers and autoscaling Infrastructure Automation Automate the deployment of GCP services using Deployment Manager or Terraform Outline the GCP Marketplace Managed Services Describe the managed services for data processing in GCP Additional course details: Nexus Humans Architecting with Google Compute Engine 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 Compute Engine 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.

GCP provides infrastructure as a service (IaaS), platform as a service (PaaS), and serverless computing environments to help businesses build, deploy, and scale applications and services.

Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Individuals preparing for the Associate Cloud Engineer examination. Recommended experience: 6 months+ hands-on experience with GCP This one-day instructor-led course helps prospective candidates structure their preparation for the Associate Cloud Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, demos, and hands-on labs, candidates will familiarize themselves with the domains covered by the examination. An Associate Cloud Engineer deploys applications, monitors operations, and manages enterprise solutions. With a shortage of cloud expertise in the job market, one which is projected to last for the next several years, Google Cloud certifications can be a way to differentiate yourself from the rest and prove you have not only the technical knowledge but the skills required to do the job. This course by itself will not prepare a candidate to pass the Associate Cloud Engineer certification exam. It will, however, help the candidate better understand the areas covered by the exam and navigate the recommended resources provided by Google and Qwiklabs for preparing to take the exam, so they can formulate their own personal study plan. This one-day instructor-led course helps prospective candidates structure their preparation for the Associate Cloud Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, demos and hands-on labs, candidates will familiarize themselves with the domains covered by the examination. An Associate Cloud Engineer deploys applications, monitors operations, and manages enterprise solutions. With a shortage of cloud expertise in the job market, one which is projected to last for the next several years, Google Cloud certifications can be a way to differentiate yourself from the rest and prove you have not only the technical knowledge, but the skills required to do the job. This course by itself will not prepare a candidate to pass the Associate Cloud Engineer certification exam. It will, however, help the candidate better understand the areas covered by the exam and navigate the recommended resources provided by Google and Qwiklabs for preparing to take the exam, so they can formulate their own personal study plan.

About this training course Business Impact: The main aim is to provide insight and understanding of data analytics and machine learning principles through applications. Field data is used to explain data-analysis workflows. Using easy to follow solution scripts, the participants will assess and extract value from the data sets. Hands-on solution approach will give them confidence to try out applicable techniques on data from their field assets. Data analysis means cleaning, inspecting, transforming, and modeling data with the goal of discovering new, useful information and supporting decision-making. In this hands-on 2-day training course, the participants learn some data analysis and data science techniques and workflows applied to petroleum production (specifically artificial lift) while reviewing code and practicing. The focus is on developing data-driven models while keeping our feet closer to the underlying oil and gas production principles. Unique Features: Eight business use cases covering their business impact, code walkthroughs for most all and solution approach. Industry data sets for participants to practice on and take home. No software or complicated Python frameworks required. Training Objectives After the completion of this training course, participants will be able to: Understand digital oil field transformation and its impact on business Examine machine learning methods Review workflows and code implementations After completing the course, participants will have a set of tools and some pathways to model and analyze their data in the cloud, find trends, and develop data-driven models Target Audience This training course is suitable and will greatly benefit the following specific groups: Artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness, and improve the facility with different data analysis techniques applied on large data sets Course Level Intermediate Advanced Training Methods The course discusses several business use-cases that are amenable to data-driven workflows. For each use case, the instructor will show the solution using a data analysis technique with Python code deployed in the Google cloud. Trainees will solve a problem and tweak their solution. Course Duration: 2 days in total (14 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts Knowledge of Python is not a must but preferred to get the full benefit. The training will use the Google Collaboratory environment available in Google-Cloud for hands-on exercises Trainees will need to bring a computer with a Google Chrome browser and a Google email account (available for free) Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

This BigQuery Conversion course is for users of SQL. We cover the interface and licensing differences and additional capabilities. Highlights of BigQuery. We will cover the general SQL topics ( see our intermediate outline ) and point out where the differences are. You would be able to move to this platform easily.

Level-7 QLS Endorsed | 22-in-1 Diploma Bundle| Free CPD PDF+ Transcript Certificate| Lifetime Access| Learner Support