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Google Cloud Platform Certification Training & Exams
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Google Cloud Engineer Associate Training
The recommended courses for Google Cloud Engineer Associate certification are "GCP Fundamentals: Core Infrastructure" , "Architecting with Google Compute Engine" and "Architecting with Google Kubernetes Engine". After completing the course one is ready to write the Google Associate Cloud Engineer certification.
For the Google Cloud Architect Professional certification one should also attend the "Architecting with Google Cloud: Design & Process"
Title:Architecting with Google Compute Engine
Duration: 3 Days
Price: R15,000 (ex vat)
Module 1
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
Module 2
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
Module 3
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
Module 4
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
Module 5
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
Module 6
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
Module 7
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
Module 8
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
- Describe Cloud DNS
Module 9
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
Module 10
Infrastructure Automation
- Automate the deployment of GCP services using Deployment Manager or Terraform
- Outline the GCP Marketplace
Module 11
Managed Services
- Describe the managed services for data processing in GCP
Title: Architecting with Google Kubernetes Engine
Duration: 3 Days
Price: R17,000 (ex vat)
Module 1
- 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
Module 2
- 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
Module 3
- Debugging, Monitoring, Logging, Error Reporting
- Introspect Kubernetes containers
- View pod logs
- Troubleshoot common Kubernetes problems
- Use Stackdriver Kubernetes Monitoring
- Use Prometheus monitoring with Stackdriver
Module 4
- 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
Module 5
- 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
Module 6
- 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
Module 7
- 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
Module 8
- Using GCP Managed Storage Services from Kubernetes Applications
- Understand pros and cons for 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
Google Data Engineer Training
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 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.
Serverless Data Analysis with Google BigQuery and Cloud Dataflow
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 a ML model locally and on cloud.
Module 10 Feature Engineering
- Creating good features.
- Transforming inputs.
- Synthetic features.
- Preprocessing with Cloud ML.
- Lab: Feature engineering.
Building Resilient Streaming Systems on Google Cloud Platform:
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.
Learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning.
Training Course Objectives:
- Architect, Design & build your data processing systems with Google Cloud Platform
- Use Cloud Dataflow to batch process or stream data with autoscaling data pipelines
- Leverage Google BigQuery to gain business insights from large datasets
- Use Tensorflow and Cloud ML to train, evaluate and predict using machine learning models
- Utilise Spark and ML APIs on Cloud Dataproc to process unstructured data
- Get real time insights from streaming data