Google Cloud Platform Training

Get Google Cloud Platform (GCP) training from an approved Google trainer partner. Whether its certification, exams, or training to skill-up your team in Google Cloud Platform (GCP) Jumping Bean has a course that suits your needs.

Build a solid and comprehensive understanding of the services and technologies available on Google Cloud to enhance your career and, if you are interested in  certification, 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.

Now that Google's data centre in South Africa is open, the demand for GCP skills will sky-rocket in Africa. The shortage of cloud computing-related skills across the world is even acute with respect to GCP;  resulting in lucrative opportunities for those with the required skills and experience. Get GCP certified and get a job.

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.

Our Clients

Absa Bank Dimension Data Teraco Discovery Health South African Revenue Service First National Bank Allan Grey multichoice CSIR Standard Bank University of Johannesburg MTN Mr Price

Google Cloud Fundamentals: Core Infrastructure

Googles  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 ensure everyone in your organisation understand 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 centers, 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 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 leverage 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.

 

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 the 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; whatever suits you we can accommodate.

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Kaofela :: Botswana Revenue Services

Training was very practical and I liked the troubleshooting skills demonstrated which will help me greatly at work

Jason :: SEDA

Very pleasant training that covered a broad range of topics which exceeded expectation

Mayshiya :: Auditor General

I loved the relevance of the training content towards my daily work and improved knowledge for making relevant recommendations

Leeto :: ABSA

Thanks for everything, experience was great and worth every cent.

Thato :: FNB

The training was great and opened interesting channels that I never knew existed.

Saleha :: Bankserv Africa

I loved the practical examples and channels explored plus the instructor created an engaging environment.

Architecting with Google Kubernetes Engine Training

This course will teach you how to create, monitor and maintain kubernetes clusters on Google Kubernetes Engine (GKE).  You will learn how to secure applications deployed to Kubernetes and to provision resources such as storage and networks for your Kubernetes applications.

Course Outline

Duration: 3 Days
Price: R16,000 (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 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

This course equips attendees with the knowledge and skill to deploy images and applications to Google Compute Engine. It will teach them how to monitor and autoscale resources automatically. The course covers IAM, access management and networking to ensure a secure environment for your organisation.

Course Outline

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

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 Google Cloud Platform

Course Outline

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.

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.

About Us

jumping bean training room

Jumping Bean is an open source integration & training company that's been delivering solutions to customers for over 20 years.

Our services include:

Linux

  • SLA support
  • Adhoc support
  • Hardening
  • Solutions architecture

Postgres

  • SLA based support
  • Implementation support
  • Solutions Architecture
  • Performance tuning 

DevOps

  • Advisory services
  • Implementation support
  • Consulting
  • SLA support
  • Training 

Big Data

  • Google & AWS Big Data support
  • Data flow architecture
  • Implementation support
  • Training

Security

  • Advisory Services
  • SOC service
  • Security Audits & Reviews
  • Training 

Software Development

  • Mobile Apps
  • Enterprise Apps 

Cloud Consulting

  • Migration
  • Cloud provider support
  • Workflow architecture
  • Automation
  • Big Data
  • Containerisation

Long Term Partnerships

We build long relationships with our customers that helps improve our understanding of their needs. We offer customised solutions & training to meet business requirements.

Our clients include large & small businesses in South Africa & across the globe.  We offer both remote and on-site support.

Passion for Technology

We are passionate about open source & pride ourselves with living on the bleeding edge of technology innovation. Our customers lean on our practical experience with emerging technologies to ensure they get the benefits of early adopters & avoid the pitfalls.

Contact Us

Please contact us for any queries via phone or our contact us form. We will be happy to answer your questions!

Appian Place, 373 Kent Ave
Ferndale,
2194
South Africa
Tel: +2711-781 8014

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