Google Cloud Platform Training & Consulting - Google Cloud Training
Google Training Courses
Get Google Cloud Platform (GCP) certification, exams, and training from certified professionals. Build a solid and comprehensive understanding of the services and technologies available on Google Cloud to enhance your career and ensure you pass your certification on your first attempt.
Check out our Official Google Training Courses
- Google Cloud Platform (GCP) - Core Infrastructure
- Google Cloud Engineer Associate Training Courses
- Google Cloud Architect Professional Training Courses
- Google Data Engineering Professional Training Courses
Why Get Google Cloud Certified?
Google Cloud Platform is one of the top three global cloud providers and with the growth of a hybrid approach to cloud service providers, GCP is certain to remain in the mix of solutions used by companies across the globe. The shortage of cloud computing-related skills across the world is even more acute with respect to GCP; resulting in lucrative opportunities for those with the required skills and experience. Google Cloud Platform provides a plethora of services that can be leveraged in a myriad of IT careers from scalable infrastructure to artificial intelligence and machine learning and data science. Get GCP certified and turbo-boost your career.
Google GSuite and Google Chrome Enterprise
GCP is built for the hybrid cloud allowing easy integration with other cloud providers and on-premises solutions that don't lock you into a single provider. But two of Google's most compelling services are G Suite and Google Chrome Enterprise. These are compelling solutions for businesses small or large with the shift to remote work and accessible-from-anywhere services. Take advantage of the huge opportunities that exist to migrate customers to GSuite and Google Chrome enterprise by getting GSuite and Google Chrome Enterprise certified.
Google Cloud Fundamentals: Core Infrastructure
Google's GCP Fundamentals Core Infrastructure course covers the fundamentals of Google Cloud Platform (GCP) and is a great starting point for anyone looking to skill up on GCP.
This one-day course ensures everyone in your organisation understands cloud concepts and the benefits cloud computing can bring. It will allow decision-makers and managers to understand core cloud concepts to make informed decisions regarding cloud migrations and solutions.
Title: GCP Fundamentals - Core Infrastructure
Duration: 1 Day
Price: R5,000 (ex vat)
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 centres, 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 the 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
Google Cloud Digital Leader Training
After attending our Cloud Digital Leader course candidates will understand the capabilities of Google Cloud core products and services and how they can be leveraged by their organizations. Candidates will also be able to articulate common business use cases and how cloud solutions support the business. The Cloud Digital Leader exam is job-role agnostic and does not require hands-on experience with Google Cloud.
Section 1: Introduction to digital transformation with Google Cloud
1.1 Explain why cloud technology is revolutionizing business
Define key terms such as cloud, cloud technology, data, and digital transformation
1.2 Explain why it is critical for businesses to adopt new technology
Compare and contrast cloud technology and traditional or on-premises technology
Describe how customer expectations have changed because of cloud technology
Identify the business and technical considerations that organizations need to think about when adopting cloud technology, including infrastructure; application and business platform modernization; the importance of data; security in the cloud
Section 2: Innovating with data and Google Cloud
2.1 Describe the role of data in digital transformation and the importance of a data-driven culture
Explain how cloud technology enables data to be applied in new ways
2.2 Identify common Google Cloud solutions for data management
Recognize examples of structured and unstructured data
2.3. Identify common Google Cloud solutions for smart analytics
Articulate the business benefits of storing data in the cloud
Apply appropriate business use cases for databases, data warehouses, and data lakes
Explain the benefits of Google Cloud data products, including: Looker, BigQuery, Cloud Spanner, Cloud SQL, Cloud Storage
2.4. Identify Google Cloud’s solutions for machine learning and AI
Define artificial intelligence (AI) and machine learning (ML)
Outline the importance of data quality in ML prediction accuracy
Describe Google Cloud’s differentiators with regard to AI and machine learning
Recognize the ways customers can use Google Cloud’s AI and ML solutions to create business value
Section 3: Infrastructure and application modernization
3.1 Learn what modernizing IT infrastructure with Google Cloud means
Explain why legacy infrastructure struggles to deliver modern services to customers
Explain the benefits of modernizing infrastructure with cloud technology
Differentiate between hybrid and multicloud infrastructures
Differentiate between virtual machines, containers, and serverless computing within business use cases
Identify the Google Cloud solutions that help businesses modernize their infrastructure
3.2 Understand modernizing applications with Google Cloud
Describe the business drivers for modernizing applications
Describe the benefits of using cloud-native applications
Apply the appropriate change pattern to different business use cases
Explain the benefits of Google Kubernetes Engine, Anthos, and App Engine for application development
3.3 Understand the value of APIs
Explain how application programming interfaces (APIs) can modernize legacy systems
Describe how APIs can create new business value
Explain the benefits of Apigee
Section 4: Understanding Google Cloud security and operations
4.1 Describe financial governance in the cloud and Google Cloud's recommended best practices for effective cloud cost management
Explain how adopting cloud technology affects the total cost of ownership (TCO)
Identify the cost management strategy needed in a given business scenario
4.2 Describe a cloud security approach and Google Cloud security benefits
Define fundamental cloud security terms, including privacy, availability, security, and control
Explain what is meant by a shared responsibility model
Describe the security benefits of using Google Cloud
Identify today's top cybersecurity challenges and threats to data privacy
Demonstrate how organizations can control and manage access to cloud resources
4.3 Explain how IT operations need to adapt to thrive in the cloud
Differentiate service availability requirements in the cloud versus in on-premises environments
Describe the operational challenges that DevOps solves
Apply the goals of site reliability engineering (SRE) to different business use cases
4.4 Identify Google Cloud solutions for cloud resource monitoring and application performance management
Explain the potential impact of unexpected or prolonged downtime
Define monitoring, logging, and observability within the context of cloud operations
Identify the Google Cloud resource monitoring and maintenance tools.
Training was very practical and I liked the troubleshooting skills demonstrated which will help me greatly at work
Very pleasant training that covered a broad range of topics which exceeded expectation
I loved the relevance of the training content towards my daily work and improved knowledge for making relevant recommendations
Thanks for everything, experience was great and worth every cent.
The training was great and opened interesting channels that I never knew existed.
I loved the practical examples and channels explored plus the instructor created an engaging environment.
Architecting with Google Kubernetes Engine Training
Duration: 3 Days
Price: R16,000 (ex vat)
- Introduction to Google Cloud Platform
- Use the Google Cloud Platform Console
- Use Cloud Shell
- Create GCP projects
- Understand the differences among GCP compute platforms
- Cloud Resource Manager, Quotas, Billing
- Launching Workloads in Kubernetes Engine
- Understand the architecture of Kubernetes: pods, namespaces
- Understand the components of Kubernetes
- Create Docker containers using Google Container Builder
- Store container images in Google Container Registry
- Create a Kubernetes Engine cluster
- Install software using Helm charts
- Debugging, Monitoring, Logging, Error Reporting
- Introspect Kubernetes containers
- View pod logs
- Troubleshoot common Kubernetes problems
- Use Stackdriver Kubernetes Monitoring
- Use Prometheus monitoring with Stackdriver
- Scheduling and Autoscaling Workloads in Kubernetes Engine
- Apply labels
- Create and manage Deployments
- Perform rolling upgrades and rollbacks of Deployments
- Define Services
- Expose Services with LoadBalancers and NodePorts
- Run cron jobs
- Control pod execution with taints and tolerations
- Configure Kubernetes Engine clusters for autoscaling
- Kubernetes and Google Cloud VPC Networking Fundamentals
- Understand the Kubernetes networking model
- Understand how Kubernetes networking differs from Docker networking
- Understand how Kubernetes networking differs from Compute Engine networking
- Understand VPC networks and subnets
- Understand load balancer types
- Use Kubernetes DNS
- Persistent Data and Storage
- Use Secrets to isolate security credentials
- Use ConfigMaps to isolate configuration artefacts
- 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 to only allow pods with specific security-related attributes to run
- Define Kubernetes network policies to allow and block traffic to pods Understand the structure of GCP IAM
- Define IAM roles and policies for Kubernetes Engine cluster administration
- Decide between building one larger cluster and many smaller clusters
- Using GCP Managed Storage Services from Kubernetes Applications
- Understand the pros and cons of using a managed storage service versus self-managed containerized storage
- Understand use cases for Cloud Storage, and use Cloud Storage from within a Kubernetes application
- Understand use cases for Cloud SQL and Cloud Spanner and use them from within a Kubernetes application
- Understand use cases for Cloud Datastore, and use Cloud Datastore from within a Kubernetes application
- Understand use cases for Bigtable, and use Bigtable from within a Kubernetes application
Architecting with Google Compute Engine Training
Duration: 3 Days
Price: R15,000 (ex vat)
Introduction to GCP
- 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
- List the VPC objects in GCP
- Differentiate between the different types of VPC networks
- Implement VPC networks and firewall rules
- Design a maintenance server
- 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
- 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
- Implement data storage services
- Describe the cloud resource manager hierarchy
- Recognize how quotas protect GCP customers
- Use labels to organize resources
- Explain the behaviour of budget alerts in GCP
- Examine billing data with BigQuery
- 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
- 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
- Describe Cloud DNS
Load Balancing and Autoscaling
- Recall the various load-balancing services
- Determine which GCP load balancer to use in specific circumstances
- Describe autoscaling behaviour
- Configure load balancers and autoscaling
- Automate the deployment of GCP services using Deployment Manager or Terraform
- Outline the GCP Marketplace
- Describe the managed services for data processing in GCP
Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform
This course is part of the Google Data Engineer specialisation courses. In this course, you will learn to create and manage computing clusters to run Hadoop, Spark, Pig, and/or Hive jobs on the Google Cloud Platform. Learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning.
You will also learn how to access various cloud storage options from their compute clusters and integrate Google's machine learning capabilities into their analytics programs. It is 5 days of jam-packed theory, demos, and practicals that will arm you with all you need to know to master the Big data on the Google Cloud Platform
Title: Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform
Duration: 4 Days
Price: R25,000 (ex vat)
Module 1 -Google Cloud Dataproc Overview
- Creating and managing clusters.
- Leveraging custom machine types and preemptible worker nodes.
- Scaling and deleting Clusters.
- Lab: Creating Hadoop Clusters with Google Cloud Dataproc.
Module 2 Running Dataproc Jobs
- Running Pig and Hive jobs.
- Separation of storage and compute.
- Lab: Running Hadoop and Spark Jobs with Dataproc.
- Lab: Submit and monitor jobs.
Module 3 Integrating Dataproc with Google Cloud Platform
- Customize the cluster with initialization actions.
- BigQuery Support.
- Lab: Leveraging Google Cloud Platform Services.
Module 4 Making Sense of Unstructured Data with Google’s Machine Learning APIs
- Google’s Machine Learning APIs.
- Common ML Use Cases.
- Invoking ML APIs.
- Lab: Adding Machine Learning Capabilities to Big Data Analysis.
Module 5 Serverless data analysis with BigQuery
- What is BigQuery.
- Queries and Functions.
- Lab: Writing queries in BigQuery.
- Loading data into BigQuery.
- Exporting data from BigQuery.
- Lab: Loading and exporting data.
- Nested and repeated fields.
- Querying multiple tables.
- Lab: Complex queries.
- Performance and pricing.
Module 6 Serverless, autoscaling data pipelines with Dataflow
- The Beam programming model.
- Data pipelines in Beam Python.
- Data pipelines in Beam Java.
- Lab: Writing a Dataflow pipeline.
- Scalable Big Data processing using Beam.
- Lab: MapReduce in Dataflow.
- Incorporating additional data.
- Lab: Side inputs.
- Handling stream data.
- GCP Reference architecture.
Serverless Machine Learning with TensorFlow on Google Cloud Platform:
Module 7 Getting started with Machine Learning
- What is machine learning (ML),
- Effective ML: concepts, types.
- ML datasets: generalization.
- Lab: Explore and create ML datasets.
Module 8 Building ML models with Tensorflow
- Getting started with TensorFlow.
- Lab: Using tf.learn.
- TensorFlow graphs and loops + lab.
- Lab: Using low-level TensorFlow + early stopping.
- Monitoring ML training.
- Lab: Charts and graphs of TensorFlow training.
Module 9 Scaling ML models with CloudML
- Why Cloud ML?
- Packaging up a TensorFlow model.
- End-to-end training.
- Lab: Run an ML model locally and on the cloud.
Module 10 Feature Engineering
- Creating good features.
- Transforming inputs.
- Synthetic features.
- Preprocessing with Cloud ML.
- Lab: Feature engineering.
Module 11 Architecture of streaming analytics pipelines
- Stream data processing: Challenges.
- Handling variable data volumes.
- Dealing with unordered/late data.
- Lab: Designing streaming pipeline.
Module 12 Ingesting Variable Volumes
- What is Cloud Pub/Sub?
- How it works: Topics and Subscriptions.
- Lab: Simulator.
Module 13 Implementing streaming pipelines
- Challenges in stream processing.
- Handle late data: watermarks, triggers, accumulation.
- Lab: Stream data processing pipeline for live traffic data.
Module 14 Streaming analytics and dashboards
- Streaming analytics: from data to decisions.
- Querying streaming data with BigQuery.
- What is Google Data Studio?
- Lab: build a real-time dashboard to visualize processed data.
Module 15 High throughput and low latency with Bigtable
- What is Cloud Spanner?
- Designing Bigtable schema.
- Ingesting into Bigtable.
- Lab: streaming into Bigtable.
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