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)

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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

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