Google Cloud Platform 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

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 leverage 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 in to a single provider. 

But two of Google 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 but getting GSuite and Google Chrome Enterprise certified.

Google Cloud Training Partners


Jumping Bean is an official Google Cloud training partner. All our instructors are official Google certified instructors. With years of hands-on training and in-field experience our trainers have a depth of knowledge and insight that will provide you with the understanding and knowledge  you need to master Google Cloud Platform.

Looking for a Google Cloud Course? Let Us Know!

Are you looking for a specific Google training  course but can't find it? Let us know and we will be happy to offer the course.  Call us on +2711-7818014 or use the contact form below.

Training That Suits You!

We offer online or classroom based training, part-time or full-time; what ever suits you we can accommodate.


What our alumni have to say about our training

Google Architect Training Courses

Google Solutions Architect Training

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

Our Clients

Our Clients






Google Data Engineering Training Course

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.
The Google Data Engineer certification course  is 5 days of jam packed theory, demos and practicals which will arm you with all you need to know to master the GCP Data Engineer certification.


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

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Tel: +2711-781 8014

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