• Developers who wish to containerize software applications.
• Administrators who are new to container technology and container orchestration.
• Architects who are considering using container technologies in software architectures.

• Knowledge of basic unix / linux commands will be good to have.
• Valid AWS account and individual credentials to create EC2 instances.
• Should have knowledge to create AWS EC2 instances and should know how to access EC2
  instances via SSH.
• Should have attended Kubernetes Essentials course by CloudThat.

• IT Professionals who want to gain complete understanding of Kubernetes.
• IT Professionals who want to understand how to prepare containerized application for Kubernetes.
• IT Professionals who want to understand the Kubernetes core API objects to configure their application.
• System Administrators who want to understand how to manage containerized applications in a cluster.

• Knowledge of basic unix / linux commands will be good to have.
• Valid AWS account and individual credentials to create EC2 instances.
• Should have knowledge to create AWS EC2 instances and should know how to access EC2 instances via SSH
• Should have attended Docker Essentials course or have very good, hands-on knowledge of Docker Containers.

• IT Professionals who wishes to learn work on highly reliable, scalable, available, cost-efficient virtual machines using Docker.
• Startups who wishes to build their IT infrastructure at a fraction of the cost of traditional methods by using containers.
• Developers interested in deploying scalable applications using Dockers.
• System administrators wishing to improve the efficiency of their servers.
• Professionals who wish to enrich their resume for prospective employers by having hands-on
  Docker experience.

• Understanding of Cloud and Virtualization concepts.
• Good Knowledge and Hands on experience with Linux.
• Some IT industry work experience or those pursuing a degree in the IT field.

• IT professionals who want to get started with a DevOps tool.
• System Administrators who are managing large scale server fleet & wants to bring in automation to their fleet.
• IT Professionals who are using on-premise or cloud servers for their application & want to reduce the time it takes to configure these servers before they can use it.
• IT Architects who want to learn about configuration automation & want to have hands-on experience on Ansible.
• System Administrators who want to manage cloud servers and networks without doing it manually.
• Decision makers who want to evaluate Ansible for their companies.

• Hands-on experience on any Linux distribution.
• Familiarity with IT environment & server administration for at least 6 months.
• Working knowledge of Bash scripting / Python.

No prerequisites for this course.

• Successful Data Analysts start this role with experience of working with data in the cloud.
• Understanding core data concepts.
• Knowledge of working with relational data in the cloud.
• Knowledge of working with non-relational data in the cloud.
• Knowledge of data analysis and visualization concepts.
• You can gain the prerequisites and a better understanding of working with data in Azure by completing Microsoft Azure Data Fundamentals before taking this course.

• Successful Data Analysts start this role with experience of working with data in the cloud.
• Understanding core data concepts.
• Knowledge of working with relational data in the cloud.
• Knowledge of working with non-relational data in the cloud.
• Knowledge of data analysis and visualization concepts.
• You can gain the prerequisites and a better understanding of working with data in Azure by completing Microsoft Azure Data Fundamentals before taking this course.

• Experience as an IT professional or student.
• Working knowledge of Microsoft Power Platform and its key components.
• Knowledge of Microsoft Dataverse (or general data modeling) and security concepts.

• Basic data modeling, user experience design, requirements analysis, and process analysis skills.
• A personal growth mindset and are comfortable using technology to solve business problems.
• An understanding of the operational need and have a vision of the desired outcome. They approach problems with phased and iterative strategies.

• Completed a role-based administrator course such as Messaging, Teamwork, Security and Compliance, or Collaboration.
• A proficient understanding of DNS and basic functional experience with Microsoft 365 services.
• A proficient understanding of general IT practices.

• Completed a role-based administrator course such as Messaging, Teamwork, Security and
  Compliance, or Collaboration.
• A proficient understanding of DNS and basic functional experience with Microsoft 365 services.
• A proficient understanding of general IT practices.

• Before attending this course, you should have experience of using Python to work with data, and some knowledge of machine learning concepts.

Before attending this course, complete the following learning path on Microsoft Learn:

• Create machine learning models.

There are no pre-requisites for taking this course. Technical IT experience is not
required however some general IT knowledge or experience would be beneficial.

Module 1: Identity and Access.

Module 2: Platform Protection.

Module 3: Security Operations.

Module 4: Data and applications.

1. Manage identities in Azure AD.
2. Manage authentication by using Azure AD.
3. Manage authorization by using Azure AD.
4. Manage application access in Azure AD.
5. Secure networking (20–25%).
6. Plan and implement security for virtual networks.
7. Plan and implement security for private access to Azure resources.
8. Plan and implement security for public access to Azure resources.
9. Secure compute, storage, and databases (20–25%).
10. Plan and implement advanced security for compute.
11. Plan and implement security for storage.
12. Plan and implement security for Azure SQL Database and Azure SQL ManagedInstance.
13. Manage security operations (25–30%).
14. Manage security posture by using Microsoft Defender for Cloud.
15. Configure and manage threat protection by using Microsoft Defender forCloud.
16. Configure and manage security monitoring and automation solutions.

Module 1: Planning for DevOps

Lessons:
• Transformation Planning.
• Project Selection.
• Team Structures.
• Migrating to Azure DevOps.

Lab: Agile Planning and Portfolio Management with Azure Boards

After completing this module, students will be able to:
• Plan for the transformation with shared goals and timelines.
• Select a project and identify project metrics and KPIs.
• Create a team and agile organizational structure.
• Design a tool integration strategy.
• Design a license management strategy (e.g. VSTS users).
• Design a strategy for end-to-end traceability from work items to working software.
• Design an authentication and access strategy.
• Design a strategy for integrating on-premises and cloud resources.
Module 2: Getting started with Source Control.

Lessons:
• What is Source Control.
• Benefits of Source Control.
• Types of Source Control Systems.
• Introduction to Azure Repos.
• Introduction to GitHub.
• Migrating from Team Foundation Version Control (TFVC) to Git in Azure Repos.
• Authenticating to Git in Azure Repos.

Lab: Version Controlling with Git

After completing this module, students will be able to:
• Describe the benefits of using Source Control.
• Describe Azure Repos and GitHub.
• Migrate from TFVC to Git.

Module 3: Scaling Git for enterprise DevOps.
Lessons:
• How to Structure your Git Repo.
• Git Branching Workflows.
• Collaborating with Pull Requests in Azure Repos.
• Why care about GitHooks.
• Fostering Inner Source.

Lab: Code Review with Pull Requests
After completing this module, students will be able to:
• Explain how to structure Git repos.
• Describe Git branching workflows.
• Leverage pull requests for collaboration and code reviews.
• Leverage Git hooks for automation.
• Use git to foster inner source across the organization.

Module 4: Consolidating Artifacts & Designing a Dependency Management Strategy
Lessons:
• Packaging Dependencies.
• Package Management.
• Migrating and Consolidating Artifacts.

Lab: Updating Packages
After completing this module, students will be able to:
• Recommend artifact management tools and practices.
• Abstract common packages to enable sharing and reuse.
• Migrate and consolidate artifacts.
• Migrate and integrate source control measures.

Module 5: Implementing Continuous Integration with Azure Pipelines
Lessons:
• The concept of pipelines in DevOps.
• Azure Pipelines.
• Evaluate use of Hosted vs Private Agents.
• Agent Pools.
• Pipelines and Concurrency.
• Azure DevOps and Open Source Projects (Public Projects).
• Azure Pipelines YAML vs Visual Designer.
• Continuous Integration Overview.
• Implementing a Build Strategy.
• Integration with Azure Pipelines.
• Integrate External Source Control with Azure Pipelines.
• Set Up Private Agents.
• Analyze and Integrate Docker Multi-Stage Builds.

Lab: Enabling Continuous Integration with Azure Pipelines.
Lab: Integrating External Source Control with Azure Pipelines.
Lab: Integrate Jenkins with Azure Pipelines.
Lab: Deploying a Multi-Container Application.

After completing this module, students will be able to:
• Implement and manage build infrastructure.
• Explain why continuous integration matters.
• Implement continuous integration using Azure DevOps.

Module 6: Managing Application Config and Secrets Lessons
• Introduction to Security.
• Implement secure and compliant development process.
• Rethinking application config data.
• Manage secrets, tokens, and certificates.
• Implement tools for managing security and compliance in a pipeline.

Lab: Integrating Azure Key Vault with Azure DevOps.

After completing this module, students will be able to:
• Manage application config and secrets.

Module 7: Managing Code Quality and Security Policies
Lessons:
• Managing Code Quality.
• Managing Security Policies.

Module 10: Design a Release Strategy.
Module 11: Set up a Release Management Workflow.
Module 12: Implement an appropriate deployment pattern.
Module 13: Implement process for routing system feedback to development teams.
Module 14: Implement a mobile DevOps strategy.
Module 15: Infrastructure and Configuration Azure Tools.
Module 16: Azure Deployment Models and Services.
Module 17: Create and Manage Kubernetes Service InfrastructureLessons.
Module 18: Third Party Infrastructure as Code Tools available with Azure.
Module 19: Implement Compliance and Security in your Infrastructure.
Module 20: Recommend and design system feedback mechanisms.
Module 21: Optimize feedback mechanisms.

Lessons:
• Select Virtual Machine Size.
• Configure High Availability.
• Implement Azure Dedicated Hosts.
• Deploy and Configure Scale Sets
• Configure Azure Disk Encryption

After completing this module, students will be able to:
• Plan for virtual machine implementations.
• Create virtual machines.
• Configure virtual machine availability, including scale sets.
• Understand High Availability options for VMs in Azure.

Module 2: Automate Deployment and Configuration of Resources
In this module, you will learn about the tools an Azure Administrator uses to manage their infrastructure. This includes the Azure Portal, Cloud Shell, Azure PowerShell, CLI, and Resource Manager Templates. This module includes:

Lessons:
• Azure Resource Manager Templates.
• Save a Template for a VM.
• Evaluate Location of New Resources.
• Configure a Virtual Hard Disk Template.
• Deploy from a Template.
• Create and Execute an Automation Runbook.

After completing this module, students will be able to:
• Leverage Azure Resource Manager to organize resources.
• Use ARM Templates to deploy resources.
• Create and Execute an Automation Runbook.
• Deploy an Azure VM from a VHD.
• Understand Azure encryption technologies.

Module 3: Implement Virtual Networking
In this module, you will learn about basic virtual networking concepts like virtual networks and subnetting, IP addressing, network security groups, Azure Firewall, and Azure DNS.

Lessons:
• Virtual Network Peering.
• Implement VNet Peering.

After completing this module, students will be able to:
• Connect services with Virtual Network Peering.
• Configure VNet Peering.
• Understand Service Chaining.
• Modify or delete VNet Peering.

Module 4: Implement Load Balancing and Network Security
In this module, you will learn about network traffic strategies including network routing and service endpoints, Azure Load Balancer, Azure Application Gateway, and Traffic Manager.

Lessons:
• Implement Azure Load Balancer.
• Implement an Application Gateway.
• Understand Web Application Firewall.
• Implement Azure Firewall.
• Implement Azure Front Door.
• Implementing Azure Traffice Manager.
• Implement Network Security Groups and Application Security Grou.
• Implement Azure Bastion.

After completing this module, students will be able to:
• Select a Load Balancer solution.
• Configure Application Gateway.
• Implement Azure Firewall.
• Create an Azure Front Door.
• Understand Traffic Manager routing methods.
• Configure Network Security Groups (NSGs).

Module 5: Implement Storage Accounts
In this module, you will learn about basic storage features including storage accounts, blob storage, Azure files and File Sync, storage security, and storage tools.

Lessons:
• Storage Accounts.
• Blob Storage.
• Storage Security.
• Managing Storage.
• Accessing Blobs and Queues using AAD.
• Configure Azure Storage Firewalls and Virtual Networks.

After completing this module, students will be able to:
• Understand Storage Account services and types.
• Configure Blob storage, accounts, containers, and access tiers.
• Implement Shared Access Signatures.
• Understand Azure Storage firewalls and virtual networks.

Module 6: Implement Azure Active Directory
In this module, you will learn how to secure identities with Azure Active Directory, and implement users and groups.

Lessons:
• Overview of Azure Active Directory.
• Users and Groups.
• Domains and Custom Domains.
• Azure AD Identity Protection.
• Implement Conditional Access.
• Configure Fraud Alerts for MFA.
• Implement Bypass Options.
• Configure Trusted IPs.
• Configure Guest Users in Azure AD.
• Manage Multiple Directori.

After completing this module, students will be able to:
• Understand how Multiple AAD organizations interact.
• Add Guest Users to Azure AD.
• Configure Location Condition Configuration.
• Configure Azure MFA settings.
• Implement Conditional Access Azure MFA.

Module 7: Implement and Manage Azure Governance
In this module, you will learn about managing your subscriptions and accounts, implementing Azure policies, and using Role-Based Access Control.

Lessons:
• Create Management Groups, Subscriptions, and Resource Groups.
• Overview of Role-Based Access Control (RBAC).
• Role-Based Access Control (RBAC) Roles.
• Azure AD Access Reviews.
• Implement and Configure an Azure Policy.
• Azure Blueprints.

After completing this module, students will be able to:
• Understand Resource Group 0rganization.
• Understand how RBAC works.
• Create an Azure AD access review.
• Create and manage policies to enforce compliance.
• Create a Blueprint.

Module 8: Implement and Manage Hybrid Identities
In this module, you will learn how to install and configure Azure AD Connect and implement Azure AD Connect Health.

Lessons:
• Install and Configure Azure AD Connect.
• Configure Password Sync and Password Writeback.
• Configure Azure AD Connect Health.

After completing this module, students will be able to:
• Implement Azure AD seamless Single Sign-On.
• Perform an Azure AD Connect installation.
• Implement Azure AD Connect Health.

Module 9: Manage Workloads in Azure
In this module, you will learn how to migrate workloads using Azure Migrate, perform VMware agent-based and agent-less migrations, and perform Azure Backup and Azure Site Recovery.

Lessons:
• Migrate Workloads using Azure Migrate.
• VMware – Agentless Migration.
• VMware – Agent-Based Migration.
• Implement Azure Backup.
• Azure to Azure Site Recovery.
• Implement Azure Update Management.

After completing this module, students will be able to:
• Understand agent-based migration architecture.
• Prepare for Azure for migration.
• Prepare an on-premises VMware environment.
• Understand Azure VM backup architecture.
• Manage updates and patches for Azure VMs.

Module 10: Implement Cloud Infrastructure Monitoring
In this module, you will learn about Azure Monitor, Azure Workbooks, Azure Alerts, Network Watcher, Azure Service Health, Azure Application Insights.

Lessons:
• Azure Infrastructure Security Monitoring.
• Azure Monitor.
• Azure Workbooks.
• Azure Alerts.
• Log Analytics.
• Network Watcher.
• Azure Service Health.
• Monitor Azure Costs.
• Azure Application Insights.
• Unified Monitoring in Azure.

Module 11: Manage Security for Applications
In this module, you will learn about Azure Key Vault and implementing authentication using Azure Managed Identities.

Lessons:
• Azure Key Vault.
• Azure Managed Identity.

After completing this module, students will be able to:
• Explain Key Vault uses such as screts, key, and Cerficate management.
• Use Managed Identities with Azure resources.

Module 12: Implement an Application Infrastructure
In this module, you will learn how to create an App Service web App for Containers, create and configure an App Service Plan, and create and manage Deployment Slots.

Lessons:
• Create and Configure Azure App Service.
• Create an App Service Web App for Containers.
• Create and Configure an App Service Plan.
• Configure Networking for an App Service.
• Create and Manage Deployment Slots.
• Implement Logic Apps.
• Implement Azure Functions.

After completing this module, students will be able to:
• Configure an Azure App Service.
• Create an App Service Plan.
• Create a Workflow using Azure Logic Apps.
• Create a Function App.

Module 13: Implement Container-Based Applications
In this module, you will learn how to run Azure Container instances and how to deploy Kubernetes with AKS.

Lessons
• Azure Container Instances.
• Configure Azure Kubernetes Service.

After completing this module, students will be able to:
• Run Azure Container instances.
• Deploy Kubernetes with AKS.

Module 14: Implement NoSQL Databases
In this module, you will learn about Azure Table Storage and recommend options for
CosmsoDB APIs.

Lessons:
• Configure Storage Account Tables.
• Select Appropriate CosmosDB APIs.

After completing this module, students will be able to:
• Outline the Table Service Data Model.
• Understand options for Azure Cosmos DB.
• Understand high availability using CosmosDB.

Module 15: Implement Azure SQL Databases
In this module, you will create an Azure SQL Database single database, create an Azure SQL Database Managed Instance, and review high-availability and Azure SQL database.

Lessons
• Configure Azure SQL Database Settings.
• Implement Azure SQL Database Managed Instances.
• High-Availability and Azure SQL Database.

After completing this module, students will be able to:
• Create an Azure SQL Database single database.
• Create an Azure SQL Database Managed Instance.
• Recommend high-availability architectural models used in Azure SQL Database.

Lessons:
• Business Opportunities for IoT
• Introduction to IoT Solution Architecture
• IoT Hardware and Cloud Services
• Lab Scenarios for this Course
Lab: Getting Started with Azure

Lab: Setting Started with Azure IoT Services
After completing this module, students will be able to:
• Explain how IoT and Azure IoT could be applied to their business
• Describe the core components of an Azure IoT Solution Architecture
• Describe the Azure IoT Services and how they relate to an IoT solution
• Create an Azure account and use the Azure portal to create an IoT Hub and DPS service

Module 2: Devices and Device Communication
In this module, students will take a closer look at the Azure IoT Hub service and will learn how to configure secure two-way communication between IoT hub and devices. Students will also be introduced to IoT Hub features such as Device Twins and IoT Hub Endpoints that will be explored in more depth as the course continues.

Lessons:
• IoT Hub and Devices.
• IoT Developer Tools.
• Device Configuration and Communication.

Lab: Setup the Development Environment
Lab: Connect IoT Device to Azure

After completing this module, students will be able to:
• Explain how IoT and Azure IoT could be applied to their business.
• Describe the core components of an Azure IoT Solution Architecture.
• Describe the Azure IoT Services and how they relate to an IoT solution.
• Create an Azure account and use the Azure portal to create an IoT Hub and DPS service.

Module 2: Devices and Device Communication
In this module, students will take a closer look at the Azure IoT Hub service and will learn how to configure secure two-way communication between IoT hub and devices. Students will also be introduced to IoT Hub features such as Device Twins and IoT Hub Endpoints that will be explored in more depth as the course continues.

Lessons:
• IoT Hub and Devices.
• IoT Developer Tools.
• Device Configuration and Communication.

Lab: Setup the Development Environment.
Lab: Connect IoT Device to Azure.

After completing this module, students will be able to:
• Explain the core features of the IoT Hub services.
• Describe the lifecycle of an Azure IoT device.
• Describe how IoT Hub manages device identities and implements other security features.
• Register devices with the IoT Hub using the Azure portal, Azure CLI, and Visual Studio Code.
• Implement the IoT Hub Device and Service SDKs.

Module 3: Device Provisioning at Scale
In this module, students will focus on device provisioning and how to configure and manage the Azure Device Provisioning Service. Students will learn about the enrollment process, auto-provisioning and re-provisioning, disenrollment, and how to implement various attestation mechanisms.

Lessons:
• Device Provisioning Service Terms and Concepts.
• Configure and Manage the Device Provisioning Service.
• Device Provisioning Tasks.

Lab: Individual Enrollment of Devices in DPS.
Lab: Automatic Enrollment of Devices in DPS.

After completing this module, students will be able to:
• Explain the process of device provisioning and the features of the Device Provisioning Service.
• Explain the security considerations associated with device provisioning and how they are managed.
• Implement the Device Provisioning Service SDKs.
• Manage the device enrollment process, including deprovisioning and disenrollment.

Module 4: Message Processing and Analytics
In this module, students will examine how IoT Hub and other Azure services can be used to process messages. Students will begin with an investigation of how to configure message and event routing and how to implement routing to built-in and custom endpoints. Students will learn about some of the Azure storage options that are common for IoT solutions. To round out his module, students will implement Azure Stream Analytics and queries for a number of ASA patterns.

Lessons:
• Messages and Message Processing.
• Data Storage Options.
• Azure Stream Analytics.

Lab: Device Message Routing.
Lab: Filtering and Aggregating Message Data.

After completing this module, students will be able to:
• Configure message and event routing.
• Route data to the built-in and custom endpoints.
• Implement message enrichment.
• Implement Azure Stream Analytics Inputs, Queries, and Outputs.
• Store message data in a warm storage for historical purposes and additional analysis.
• Use an Azure Function within a message processing and analytics solution.

Module 5: Insights and Business Integration
In this module, students will learn about the Azure services and other Microsoft tools that can be used to generate business insights and enable business integration.
Students will implement Azure Logic Apps and Event Grid, and they will configure the connection and data transformations for data visualization tools such as Time Series Insights and Power BI.

Lessons:
• Business Integration for IoT Solutions.
• Data Visualization with Time Series Insights.
• Data Visualization with Power BI.

Lab: Integrate IoT Hub with Event Grid.
Lab: Explore and Analyze Time Stamped Data with Time Series Insights.

After completing this module, students will be able to:
• Explain the options for business integration within an IoT solution and how to achieve them.
• Develop business integration support using Logic Apps and Event Grid.
• Configure IoT Data for Visualization in Time Series Insights.
• Configure IoT Data for Visualization in Power BI.

Module 6: Azure IoT Edge Deployment Process
In this module, students will learn how to deploy a module to an Azure IoT Edge device. Students will also learn how to configure and use an IoT Edge device as a gateway device.

Lessons:
• Introduction to Azure IoT Edge.
• Edge Deployment Process.
• Edge Gateway Devices.

Lab: Introduction to IoT Edge.
Lab: Set Up an IoT Edge Gateway.

After completing this module, students will be able to:
• Describe the difference between an IoT device and an IoT Edge device.
• Configure an IoT Edge device.
• Implement an IoT Edge deployment using a deployment manifest.
• Configure an IoT Edge device as a gateway device.

Module 7: Azure IoT Edge Modules and Containers
In this module, students will develop and deploy custom edge modules, and will implement support for an offline scenario that relies on local storage. Students will use Visual Studio Code to build custom modules as containers using a supported container engine.

Lessons:
• Develop Custom Edge Modules.
• Offline and Local Storage.

Lab: Develop, Deploy, and Debug a Custom Module on Azure IoT Edge.
Lab: Run an IoT Edge Device in Restricted Network and Offline.

After completing this module, students will be able to:
• Explain the requirements for building a custom edge module.
• Configure Visual Studio Code for developing containerized modules.
• Deploy a custom module to an IoT Edge device.
• Implement local storage on an IoT Edge device in support of an offline scenario.

Module 8: Device Management
In this module, students will learn how to implement device management for their IoT solution. Students will develop device management solutions that use devoice twins and solutions that use direct methods.

Lessons:
• Introduction to IoT Device Management.
• Manage IoT and IoT Edge Devices.
• Device Management at Scale.

Lab: Remotely Monitor and Control Devices with Azure IoT Hub.
Lab: Automatic Device Management.

After completing this module, students will be able to:
• Describe the most common device management patterns and configuration best practices.
• Describe when and how to use device twins and direct methods to implement device management.
• Implement device management for various patterns using device twins and direct methods.
• Implement device management at scale using automatic device management and jobs.

Module 9: Solution Testing, Diagnostics, and Logging
In this module, students will configure logging and diagnostic tools that help developers to test their IoT solution. Students will use IoT Hub and Azure Monitor to configure alerts and track conditions such as device connection state that can be used to troubleshoot issues.

Lessons:
• Monitoring and Logging.
• Troubleshooting.

Lab: Configure Metrics and Logs in Azure IoT Hub.
Lab: Monitor and Debug Connection Failures.

After completing this module, students will be able to:
• Describe the options for monitoring and logging an Azure IoT solution.
• Configure Azure Monitor to support of an IoT solution.
• Configure IoT Hub Metrics to support of an IoT solution.
• Implement diagnostics logging.
• Troubleshoot IoT device connection and communication issues.

Module 10: Azure Security Center and IoT Security Considerations
In this module, students will examine the security considerations that apply to an IoT solution. Students will begin by investigating security as it applies to the solution architecture and best practices, and then look at how Azure Security Center for IoT supports device deployment and IoT Hub integration. Students then use Azure Security Center for IoT Agents to enhance the security of their solution.

Lessons:
• Security Fundamentals for IoT Solutions.
• Introduction to Azure Security Center for IoT.
• Enhance Protection with Azure Security Center for IoT Agents.

Lab: Implementing Azure Security Center for IoT
After completing this module, students will be able to:
• Describe security concerns and best practices for an IoT solution.
• Describe the Azure IoT Security Architecture and Threat Modeling.
• Describe the features and support provided by Azure Security Center for IoT.
• Configure Security Agents and Security Module Twins.
• Aggregate Azure Security Center for IoT Events.

Module 11: Build an IoT Solution with IoT Central
In this module, students will learn how configure and implement Azure IoT Central as a SaaS solution for IoT. Students will begin with a high-level investigation of IoT Central and how it works. With a basic understanding of IoT central establish, students will move on to creating and managing device templates, and then
managing devices in their IoT Central application.

Lessons:
• Introduction to IoT Central.
• Create and Manage Device Templates.
• Manage Devices in Azure IoT Central.

Lab: Get Started with Azure IoT Central.
Lab: Implementing IoT Solutions with Azure IoT Central.

After completing this module, students will be able to:
• Describe the difference between Azure IoT Central and the Azure IoT PaaS services.
• Describe the features provided by Azure IoT Central.
• Describe the purpose and components of a Device Template.
• Create and publish a Device Template.
• Manage devices using rules and notifications.
• Mange devices at scale using jobs.

Module 1: Creating Azure App Service Web Apps

Students will learn how to build a web application on the Azure App Service platform. They will learn how the platform functions and how to create, configure, scale, secure, and deploy to the App Service platform.

Lessons:
• Azure App Service core concepts.
• Creating an Azure App Service Web App.
• Configuring and Monitoring App Service apps.
• Scaling App Service apps.
• Azure App Service staging environments.

Module 2: Implement Azure functions

This module covers creating Functions apps, and how to integrate triggers and inputs/outputs into the app.

Lessons:
• Azure Functions overview.
• Developing Azure Functions.
• Implement Durable Functions.

Module 3: Develop solutions that use blob storage
Students will learn how Azure Blob storage works, how to manage data through the hot/cold/archive blob storage lifecycle, and how to use the Azure Blob storage client library to manage data and metadata.

Lessons:
• Azure Blob storage core concepts.
• Managing the Azure Blob storage lifecycle.
• Working with Azure Blob storage.

Module 4: Develop solutions that use Cosmos DB storage
Students will learn how Cosmos DB is structured and how data consistency is managed. Students will also learn how to create Cosmos DB accounts and create databases, containers, and items by using a mix of the Azure Portal and the .NET SDK.

Lessons:
• Azure Cosmos DB overview.
• Azure Cosmos DB data structure.
• Working with Azure Cosmos DB resources and data.

Module 5: Implement IaaS solutions
This module instructs students on how to use create VMs and container images to use in their solutions. It covers creating VMs, using ARM templates to automate resource deployment, create and manage Docker images, publishing an image to the Azure Container Registry, and running a container in Azure Container Instances.

Lessons:
• Provisioning VMs in Azure.
• Create and deploy ARM templates.
• Create container images for solutions.
• Publish a container image to Azure Container Registry.
• Create and run container images in Azure Container Instances.

Module 6: Implement user authentication and authorization
Students will learn how to leverage the Microsoft Identity Platform v2.0 to manage authentication and access to resources. Students will also learn how to use the Microsoft Authentication Library and Microsoft Graph to authenticate a user and retrieve information stored in Azure, and how and when to use Shared Access Signatures.

Lessons:
• Microsoft Identity Platform v2.0.
• Authentication using the Microsoft Authentication Library.
• Using Microsoft Graph.
• Authorizing data operations in Azure Storage.

Module 7: Implement secure cloud solutions
This module covers how to secure the information (keys, secrets, certificates) an application uses to access resources. It also covers securing application configuration information.
Lessons:
• Manage keys, secrets, and certificates by using the KeyVault API.
• Implement Managed Identities for Azure resources.
• Secure app configuration data by using Azure App Configuration.

Module 8: Implement API Management
Students will learn how to publish APIs, create policies to manage information shared through the API, and to manage access to their APIs by using the Azure API Management service.

Lessons:
• API Management overview
• Defining policies for APIs
• Securing your APIs

Module 9: Develop App Service Logic Apps
This module teaches students how to use Azure Logic Apps to schedule, automate, and orchestrate tasks, business processes, workflows, and services across enterprises or organizations.

Lessons:
• Azure Logic Apps overview.
• Creating custom connectors for Logic Apps.

Module 10: Develop event-based solutions
Students will learn how to buildapplications with event-based architectures.

Lessons:
• Implement solutions that use Azure Event Grid.
• Implement solutions that use Azure Event Hubs.
• Implement solutions that use Azure Notification Hubs.

Module 11: Develop message-based solutions
Students will learn how to build applications with message-based architectures.

Lessons:
• Implement solutions that use Azure Service Bus.
• Implement solutions that use Azure Queue Storage queues.

Module 12: Monitor and optimize Azure solutions
This module teaches students how to instrument their code for telemetry and how to analyze and troubleshoot their apps.
Lessons:
• Overview of monitoring in Azure.
• Instrument an app for monitoring.
• Analyzing and troubleshooting apps.
• Implement code that handles transient faults.

Module 13: Integrate caching and content delivery within solutions
Students will learn how to use different caching services to improve the performance of their apps.

Lessons:
• Develop for Azure Cache for Redis.
• Develop for storage on CDNs.

Day 1

Module 1: Introduction

Contains an overview of the SAP and Microsoft partnership.

Module 2: Foundations of SAP on Azure

Contains brief lessons on Azure compute, Azure storage, Azure networking, SAP HANA for Azure (Large Instances), identity services, governance and manageability, backup and data protection services, and migration services.

Module 3: SAP Certified Offerings on Azure
Contains lessons on general prerequisites (SAP support in public cloud environments), deployment options of Azure for SAP workloads, SAP product-specific support for Azure, operating System support of Azure for SAP workloads, storage support of Azure for SAP workloads, networking support for SAP, database support for SAP, high availability and disaster recovery support for SAP, and monitoring requirements for SAP.

Lab: Online Lab: Implementing Linux clustering on Azure VMs.
Lab: Online Lab: Implementing Windows clustering on Azure VMs.

Day 2
Module 4: SAP on Azure Reference Architecture
Contains lessons on SAP NetWeaver with AnyDB, SAP S4 HANA, and SAP HANA on Azure (Large Instances) on Azure VMs.

Module 5: Planning for Implementing SAP Solutions on Azure
Contains lessons on Azure VM compute, network, and storage considerations. As well as Azure VM high availability and disaster recovery, Azure VM backup considerations, Azure VM monitoring considerations, Azure VM security considerations, and Azure VM authentication and access control considerations.

Module 6: Planning for Migrating SAP Workloads to Azure
Contains lessons on strategies for migrating SAP systems to Microsoft Azure and SAP a workload planning and deployment checklist.

Day 3
Module 7: Implementing Azure VM-based SAP Solutions
Contains lessons on Azure VM deployment methodologies, single-instance implementations (2tier or 3-tier), implementing HA SAP NetWeaver with AnyDB on Azure VMs, implementing HA SAP HANA on Azure VMs, configure the Azure Enhanced Monitoring Extension for SAP, and implementing AD and Azure AD-based authentication.

Module 8: Module 8-Deploying HANA Large Instances (HLI)

Contains a lesson on implementing HANA Large Instances (HLI).

Module 9: Migrating SAP Workloads to Azure
Contains lessons on migration options, DMO methodologies, cloud migration options, and Very Large Database Migration to Azure.

Lab: Online Lab-Implement SAP architecture on Azure VMs running Linux
Lab: Online Lab-Implement SAP architecture on Azure VMs running Windows

Module 10: Maintaining SAP on Azure
Contains lessons on, remote management, performing backups and restores, OS and workload updates, vertical and horizontal scaling, and Disaster Recovery (DR).

Module 11: Monitoring and Troubleshooting SAP on Azure
Contains lessons on monitoring Azure VMs, monitoring SAP HANA on Azure (Large Instances), and integrating SAP solutions with Microsoft cloud services.

• To build AI solutions on Azure.
• To learn about various Azure Machine Learning services. It’s usage & integration.
• To understand the profound impacts of how Machine Learning is making in smart business decisions.
• To deploy AI solutions in Azure.

• Candidates with both technical and nontechnical backgrounds.

Prerequisites:

• Should have general programming knowledge or experience.
• Should have a foundational knowledge of Machine Learning (ML) and Artificial Intelligence (AI) concepts.
• Should have knowledge of Microsoft Azure services.

• Understand the AWS infrastructures as it relates to system operations such as the global infrastructure,
  core services and account security.
• Use the AWS Command Line Interface, and understand additional administration and development
  tools.
• Manage, secure, and scale compute instances on AWS.
• Identify container services and services available for serverless computing.
• Build virtual private networks with Amazon VPC.
• Configure and manage storage options utilizing the storage services offered with AWS.
• Monitor the health of your infrastructure with services such as Amazon CloudWatch, AWS CloudTrail
  and AWS Config.
• Manage resource consumption in an AWS account using tags, Amazon CloudWatch, and AWS Trusted
  Advisor.
• Create and configure automated and repeatable deployments with tools such as AMIs and AWS
  CloudFormation.

Intended audience:
This course is intended for:

• Systems Administrators and Software Developers, especially those in DevOps roles.

Prerequisites:

We recommend that attendees of this course have:

• Successfully completed the AWS Technical Essentials classroom training.
• A background in either software development or systems administration.
• Proficiency in maintaining operating systems at the command line, such as shell scripting in Linux
  environments or cmd/PowerShell in Windows.
• Basic knowledge of networking protocols (TCP/IP, HTTP).

• Systems operations in the Cloud.
• AWS Global Infrastructure.
• Introduction to core services.
• AWS Account Security.
• AWS Identity and Access Management (IAM).
• Demonstration: Deploying a website.

Module 2: Tooling and Automation

• AWS Command Line Interface (CLI).
• AWS System Manager.
• Additional administration and development tools.
• AWS Software Development Kit (SDK).
• AWS Cloud Formation.
• AWS OpsWorks.
• Hands-on lab 1: Using AWS System Manager.

Module 3: Computing (Servers)

• Computing on AWS.
• Managing your AWS instances.
• Securing your AWS instances.
• AWS EC2 instance pricing.
• Hands-on lab 2: Creating Amazon EC2 instances.

Module 4: Computing (scaling and name resolution)

• Elastic load balancing.
• Amazon EC2 Auto Scaling.
• Amazon Route 53.
• Hands-on lab 3: Using Auto Scaling in AWS.

Day Two
Module 5: Computing (Containers and Serverless)

• Containers on AWS.
• AWS Lambda.
• Amazon API Gateway.
• AWS Batch.

Module 6: Computing (Database Services)

• Amazon Relational Database Service (Amazon RDS).
• Amazon Aurora.
• Amazon DynamoDB.
• AWS Database Migration Services.

Module 7: Networking

• Networking and Amazon Virtual Private Cloud (VPC)
• Securing your network.
• Troubleshooting networks in AWS.
• Hands-on lab 4: Configuring a Virtual Private Cloud (Windows & Linux).

Module 8: Storage and archiving

• Amazon Elastic Block Store ( Amazon EBS).
• Amazon Store.
• Amazon Elastic File System (Amazon EFS).
• Amazon Simple Storage Services (Amazon S3).
• Amazon S3 Glacier.
• AWS Snowball.
• Hands-on lab 5: Managing Storage in AWS (Windows & Linux).
• Hands-on lab 8: Automating deployments with Cloud Formation.

• Use the AWS shared security responsibility model.
• Architect and build AWS application infrastructures that are protected against the most common
  security threats.
• Use encryption to protect data at rest and in transit.
• Apply security checks and analyses in an automated and reproducible way.
• Configure authentication for resources and applications in the AWS Cloud.
• Gain insight into events by capturing, monitoring, processing, and analyzing logs.
• Identify and mitigate incoming threats against applications and data.
• Perform security assessm.

Intended audience:
This course is intended for:

• Security Engineers.
• Security Architects.
• Information Security professionals.

Prerequisites:
We recommend that attendees of this course have:

• Working knowledge of IT security practices and infrastructure concepts.
• Familiarity with cloud computing concepts.
• Completed AWS Cloud Practitioner Essentials (Second Edition) free digital course or classroom course, AWS
  Security Fundamentals digital training, and Architecting on AWS classroom training.

• Security in the AWS Cloud.
• AWS Shared Responsibility Model.
• Incident response overview.
• DevOps with security engineering.

Module 1: Identifying entry points on AWS

• Identify the different ways to access the AWS platform.
• Understanding IAM policies.
• IAM permissions boundary.
• Multi-factor authentication.
• AWS CloudTrail.
• Hands-on lab 1: Cross-account access.

Module 2: Security considerations: web application environments

• Threats in a three-tier architecture.
• Common threats: User access.
• Common threats: Data access.
• AWS Trusted Advisor.

Module 3: Application security

• Dedicated Amazon EC2 instances and hosts.
• Amazon machine images (AMIs).
• Amazon Inspector.
• AWS Systems Manager.
• Hands-on lab 2: Using AWS Systems Manager and Amazon Inspector.

Module 4: Securing network communications – part 1

• Amazon VPC security considerations.
• Responding to compromised instances.
• Elastic Load Balancing.
• AWS Certificate Manager (ACM).

Day Two
Module 5: Data security

• Data protection strategies.
• Encryption on AWS.
• Protecting data at rest with Amazon S3, Amazon RDS, and Amazon DynamoDB.
• Protecting archived data with Amazon S3 Glacier.

Module 6: Security considerations: hybrid environments

• AWS site-to-site and client VPN connections.
• AWS Direct Connect (DX).
• AWS Transit Gateway.
• AWS Storage Gateway.

Module 7: Monitoring and collecting logs on AWS

• Amazon CloudWatch and CloudWatch Logs.
• AWS Config.
• Amazon CloudWatch logs.
• Amazon VPC Flow logs.
• Amazon S3 server access logs.
• ELB access logs.
• Hands-on lab 3 part 1: Server log analysis – log collection.

Module 8: Processing Logs on AWS

• Amazon Kinesis for log processing.
• Amazon Athena for log processing.
• Hands-on lab 3 part 2: Server log analysis – log analysis.

Module 9: Securing network communications – part 2

• Amazon VPC peering.
• Amazon VPC endpoints.

Module 10: Out-of-region protection

• Denial of service threats overview.
• Amazon Route 53.
• AWS WAF.
• Amazon CloudFront.
• AWS Shield.
• AWS Firewall Manager.
• DDoS mitigation on AWS.

Day Three
Module 11: Account management on AWS

• AWS Organizations.
• AWS Control Tower.
• AWS Single Sign-On (AWS SSO).
• AWS Directory Service.
• Hands-on lab 4: Federated access with ADFS.

Module 12: Security considerations: serverless environments

• Amazon Cognito.
• Amazon API Gateway.
• Secure messaging with Amazon SQS and Amazon SNS.
• AWS Lambda.
• Hands-on lab 5: Monitor and respond with AWS Lambda and AWS Config.

Module 13: Secrets Management on AWS

• AWS Key Management Service (AWS KMS).
• AWS CloudHSM.
• AWS Secrets Manager.
• Hands-on lab 6: Using AWS KMS.

Module 14: Automating security on AWS

• AWS CloudFormation.
• AWS Service Catalog.
• Hands-on lab 7: Security automation on AWS with AWS Service Catalog.

Module 15: Threat detection and sensitive data monitoring

• Amazon GuardDuty.
• Amazon Macie.

• Use DevOps best practices to develop, deliver, and maintain applications and services at high velocity on
  AWS

• List the advantages, roles and responsibilities of small autonomous DevOps teams.
• Design and implement an infrastructure on AWS that supports DevOps development projects.
• Leverage AWS Cloud9 to write, run and debug your code.
• Deploy various environments with AWS CloudFormation.
• Host secure, highly scalable, and private Git repositories with AWS CodeCommit.
• Integrate Git repositories into CI/CD pipelines.
• Automate build, test, and packaging code with AWS CodeBuild.
• Securely store and leverage Docker images and integrate them into your CI/CD pipelines.
• Build CI/CD pipelines to deploy applications on Amazon EC2, serverless applications, and container-based
  applications.
• Implement common deployment strategies such as “all at once,” “rolling,” and “blue/green”.
• Integrate testing and security into CI/CD pipelines.
• Monitor applications and environments using AWS tools and technologies.

Intended audience:

This course is intended for:

• DevOps engineers.
• DevOps architects.
• Operations engineers.
• System administrators.
• Developers.

Prerequisites:

We recommend that attendees of this course have:

• Previous attendance at the Systems Operations on AWS or Developing on AWS courses.
• Working knowledge of one or more high-level programing languages, such as C#, Java, PHP, Ruby, Python.
• Intermediate knowledge of administering Linux or Windows systems at the command-line level.
• Two or more years of experience provisioning, operating, and managing AWS environments.

Day One
Module 0: Course overview

• Course objective.
• Suggested prerequisites.
• Course overview breakdown.

Module 1: Introduction to DevOps

• What is DevOps?.
• The Amazon journey to DevOps.
• Foundations for DevOps.

Module 2: Infrastructure automation

• Introduction to Infrastructure Automation.
• Diving into the AWS CloudFormation template.
• Modifying an AWS CloudFormation template.
• Demonstration: AWS CloudFormation template structure, parameters, stacks, updates, importing
  resources, and drift detection.

Module 3: AWS toolkits

• Configuring the AWS CLI.
• AWS Software Development Kits (AWS SDKs).
• AWS SAM CLI.
• AWS Cloud Development Kit (AWS CDK).
• AWS Cloud9.
• Demonstration: AWS CLI and AWS CDK.
• Hands-on lab: Using AWS CloudFormation to provision and manage a basic infrastructure.

Module 4: Continuous integration and continuous delivery (CI/CD) with development tools

• CI/CD Pipeline and Dev Tools.
• Demonstration: CI/CD pipeline displaying some actions from AWS CodeCommit, AWS CodeBuild, AWS CodeDeploy and AWS CodePipeline.
• Hands-on lab: Deploying an application to an EC2 fleet using AWS CodeDeploy.

Day Two
Module 4: Continuous integration and continuous delivery (CI/CD) with development tools (continued)

• AWS CodePipeline
• Demonstration: AWS integration with Jenkins
• Hands-on lab: Automating code deployments using AWS CodePipeline

Module 5: Introduction to Microservices

Module 6: DevOps and containers

• Deploying applications with Docker.
• Amazon Elastic Container Service and AWS Fargate.
• Amazon Elastic Container Registry and Amazon Elastic Kubernetes service.
• Demonstration: CI/CD pipeline deployment in a containerized application.

Module 7: DevOps and serverless computing

• AWS Lambda and AWS Fargate.
• AWS Serverless Application Repository and AWS SAM.
• AWS Step Functions.
• Demonstration: AWS Lambda and characteristics.
• Demonstration: AWS SAM quick start in AWS Cloud9.
• Hands-on lab: Deploying a serverless application using AWS Serverless Application Model (AWS
  SAM) and a CI/CD Pipeline.

Module 8: Deployment strategies

• Continuous Deployment.
• Deployments with AWS Services.

Module 9: Automated testing

• Introduction to testing.
• Tests: Unit, integration, fault tolerance, load, and synthetic.
• Product and service integrations.

Module 10: Security automation

• Introduction to DevSecOps.
• Security of the Pipeline.
• Security in the Pipeline.
• Threat Detection Tools.
• Demonstration: AWS Security Hub, Amazon GuardDuty, AWS Config, and Amazon Inspector.

Module 11: Configuration management

• Introduction to the configuration management process.
• AWS services and tooling for configuration management.
• Hands-on lab: Performing blue/green deployments with CI/CD pipelines and Amazon Elastic Container Service (Amazon ECS).

Module 12: Observability

• Introduction to observability.
• AWS tools to assist with observability.
• Hands-on lab: Using AWS DevOps tools for CI/CD pipeline automations.

Module 13: Reference architecture (Optional module)

• Reference architectures.

Module 14: Course summary

• Components of DevOps practice.
• CI/CD pipeline review.
• AWS Certification.

• Set up the AWS SDK and developer credentials for Java, C#/.NET, Python, and JavaScript.
• Interact with AWS services and develop solutions by using the AWS SDK.
• Use AWS Identity and Access Management (IAM) for service authentication.
• Use Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB as data stores.
• Integrate applications and data by using AWS Lambda, Amazon API Gateway, Amazon Simple Queue
  Service (Amazon SQS), Amazon Simple Notification Service (Amazon SNS), and AWS Step Functions.
• Use Amazon Cognito for user authentication.
• Use Amazon ElastiCache to improve application scalability.
• Use containers in the development process.
• Leverage the CI/CD pipeline to deploy applications on AWS.

Intended audience
This course is intended for:

• Intermediate-level software developers.

Prerequisites:
We recommend that attendees of this course have:

• In-depth knowledge of at least one high-level programming language.
• Working knowledge of core AWS services and public cloud implementation.

• Introduction to AWS.
• Cloud scenarios.
• Infrastructure overview.

Module 2: Introduction to Developing on AWS

• Getting started developing on AWS.
• Introduction to developer tools.
• Introduction to management tools.

Module 3: Introduction to IAM

• Shared responsibility model.
• Introduction to IAM.
• User authentication and authorization.

Module 4: Introduction to the lab

• Get access to the lab.
• Choose your language.
• Choose your dev instance.
• Connect to your dev instance.
• Hands-on lab 1: Ready, set, go! Programming languages: Java, .Net, Python ; OS: Windows, Linux.

Module 5: Developing solutions with Amazon S3

• Overview of AWS storage options.
• Key concepts.
• Best practices.
• Troubleshooting.
• Course scenario.
• Hands-on lab 2: Developing storage solutions with Amazon S3.

Day Two
Module 6: Developing flexible NoSQL solutions with Amazon DynamoDB

• Introduction to AWS Database Options.
• Amazon DynamoDB.
• Developing with Amazon DynamoDB.
• Best practices.
• Troubleshooting.
• Course scenario.
• Hands-on lab 3: Developing flexible NoSQL solutions with Amazon DynamoDB.

Module 7: Developing solutions with AWS Lambda

• What is serverless computing?.
• Introduction to AWS Lambda.
• How AWS Lambda works.
• Use cases.
• Best practice.
• Course scenario.

Module 8: Developing solutions with Amazon API

• Introduction to Amazon API Gateway.
• Developing with Amazon API Gateway.
• Best practices.
• Introduction to AWS Serverless Application Model (SAM).
• Course scenario.
• Hands-on lab 4: Developing Event-Driven Solutions with AWS Lambda.

Module 9: Developing solutions with Amazon SQS &SNS

• Why use a queueing service?.
• Developing with Amazon Simple Queue Service (SQS).
• Developing with Amazon Simple Notification Service (SNS).
• Developing with Amazon MQ.
• Hands-on lab 5: Developing messaging solutions with Amazon SQS and SNS.

Module 10: Developing solutions with AWS Step Functions

• Understanding the need for AWS Step Functions.
• Introduction to AWS Step Functions.
• AWS Step Functions use case.

Day Three
Module 11: Caching information with Amazon ElastiCache

• Caching overview.
• Caching with Amazon ElastiCache.
• Caching strategy.

Module 12: Getting started with containers

• Why containers?.
• Introduction to Docker containers.
• Getting started with Docker.
• Introduction to Amazon Container Services.
• Hands-on lab 6: Getting started with Docker containers OS: Linux.

Module 13: Developing secure solutions

• Securing your applications.
• Authenticating your applications to AWS.
• Authenticating your customers.
• Course scenario.

Module 14: Deploying Applications

• Introduction to DevOps.
• Introduction to deployment and testing strategies.
• Deploying applications with AWS Beanstalk.
• Course scenario.

This course is designed to teach you how to:

• Discuss the core concepts of data warehousing, and the intersection between data warehousing and big
  data solutions.
• Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a
  data warehouse in the cloud.
• Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis,
  and Amazon S3, to contribute to the data warehousing solution.
• Architect the data warehouse.
• Identify performance issues, optimize queries, and tune the database for better performance.
• Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket.
• Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse.

Intended audience:
This course is intended for:

• Database Architects, Database Administrators, Database Developers, Data Analysts and Data Scientists.

Prerequisites:
We recommend that attendees of this course have:

• Familiarity with relational databases and database design concepts.

• Relational databases
• Data warehousing concepts
• The intersection of data warehousing and big data
• Overview of data management in AWS
• Hands-on lab 1: Introduction to Amazon Redshift.

Module 2: Introduction to Amazon Redshift

• Conceptual overview.
• Real-world use cases.
• Hands-on lab 2: Launching an Amazon Redshift cluster.

Module 3: Launching clusters

• Building the cluster.
• Connecting to the cluster.
• Controlling access.
• Database security.
• Load data.
• Hands-on lab 3: Optimizing database schemas.

Day Two
Module 4: Designing the database schema

• Schemas and data types.
• Columnar compression.
• Data distribution styles.
• Data sorting methods.

Module 5: Identifying data sources

• Data sources overview
• Amazon S3
• Amazon DynamoDB
• Amazon EMR
• Amazon Kinesis Data Firehose
• AWS Lambda Database Loader for Amazon Redshift
• Hands-on lab 4: Loading real-time data into an Amazon Redshift database.

Module 6: Loading data

• Preparing Data.
• Loading data using COPY.
• Maintaining tables.
• Concurrent write operations.
• Troubleshooting load issues.
• Hands-on lab 5: Loading data with the COPY command.

Day Three
Module 7: Writing queries and tuning for performance

• Amazon Redshift SQL.
• User-Defined Functions (UDFs).
• Factors that affect query performance.
• The EXPLAIN command and query plans.
• Workload Management (WLM).
• Hands-on lab 6: Configuring workload management.

Module 8: Amazon Redshift Spectrum

• Amazon Redshift Spectrum.
• Configuring data for Amazon Redshift Spectrum.
• Amazon Redshift Spectrum Queries.
• Hands-on lab 7: Using Amazon Redshift Spectrum.

Module 9: Maintaining clusters

• Audit logging.
• Performance monitoring.
• Events and notifications.
• Lab 8: Auditing and monitoring clusters.
• Resizing clusters.
• Backing up and restoring clusters.
• Resource tagging and limits and constraints.
• Hands-on lab 9: Backing up, restoring and resizing clusters.

Module 10: Analyzing and visualizing data

• Power of visualizations.
• Building dashboards.
• Amazon QuickSight editions and features.

In this course, you will learn how to:
This course is designed to teach you how to:

Use Apache Hadoop with Amazon EMR

• Launch and configure an Amazon EMR cluster.
• Use common programming frameworks for Amazon EMR, including Hive, Pig, and Streaming.
• Use Hue to improve the ease-of-use of Amazon EMR.
• Use in-memory analytics with Spark on Amazon EMR.
• Understand how services like AWS Glue, Amazon Kinesis, Amazon Redshift, Amazon Athena, and.
• Amazon QuickSight can be used with big data workloads.

Intended audience:
This course is intended for:

• Individuals responsible for designing and implementing big data solutions, namely Solutions Architects
  and SysOps Administrators.
• Data Scientists and Data Analysts interested in learning about big data solutions on AWS.

Prerequisites:
We recommend that attendees of this course have:

• Basic familiarity with big data technologies, including Apache Hadoop, HDFS, and SQL/NoSQL querying.
• Completed Data Analytics Fundamentals free digital training or equivalent experience.
• Working knowledge of core AWS services and public cloud implementation.
• Completed the AWS Technical Essentials classroom training or have equivalent experience.
• Basic understanding of data warehousing, relational database systems, and database design.

Day One
Module 1: Overview of Big Data

• What is big data.
• The big data pipeline.
• Big data architectural principals.

Module 2: Big Data ingestion and transfer

• Overview: Data ingestion.
• Transferring data.

Module 3: Big data streaming and Amazon Kinesis

• Stream processing of big data.
• Amazon Kinesis.
• Amazon Kinesis Data Firehose.
• Amazon Kinesis Video Streams.
• Amazon Kinesis Data Analytics.
• Hands-on lab 1: Streaming and Processing Apache Server Logs Using Amazon Kinesis.

Module 4: Big data storage solutions

• AWS data storage options.
• Storage solutions concepts.
• Factors in choosing a data store.

Module 5: Big data processing and analytics

• Big data processing and analytics.
• Amazon Athena.
• Hands-on lab 2: Using Amazon Athena to Analyze Log Data.

Day Two
Module 6: Apache Hadoop and Amazon EMR

• Introduction to Amazon EMR and Apache Hadoop.
• Best practices for ingesting data.
• Amazon EMR.
• Amazon EMR architecture.
• Hands-on lab 3: Storing and Querying Data on Amazon DynamoDB.

Module 7: Using Amazon EMR

• Developing and running your application.
• Launching your cluster.
• Handling output from your completed jobs.

Module 8: Hadoop programming frameworks

• Hadoop frameworks.
• Other frameworks for use on Amazon EMR.
• Hands-on lab 4: Processing Server Logs with Hive on Amazon EMR.

Module 9: Web interfaces on Amazon EMR

• Hue on Amazon EMR.
• Monitoring your cluster.
• Hands-on lab 5: Running Pig Scripts in Hue on Amazon EMR.

Module 10: Apache Spark on Amazon EMR

• Apache Spark.
• Using Spark.
• Hands-on lab 6: Processing NY Taxi Data Using Apache Spark.

Day Three
Module 11: Using AWS Glue to automate ETL workloads

• What is AWS Glue?.
• AWS Glue: Job orchestration.

Module 12: Amazon Redshift and big data

• Data warehouses vs. traditional databases.
• Amazon Redshift.
• Amazon Redshift architecture.

Module 13: Securing your Amazon deployments

• Securing your Amazon deployments.
• Amazon EMR security overview.
• AWS Identity and Access Management (IAM) overview.
• Securing data.
• Amazon Kinesis security overview.
• Amazon DynamoDB security overview.
• Amazon Redshift security overview.

Module 14: Managing big data costs

• Total cost considerations for Amazon EMR.
• Amazon EC2 pricing models.
• Amazon Kinesis pricing models.
• Cost considerations for Amazon DynamoDB.
• Cost considerations and pricing models for Amazon Redshift
• Optimizing cost with AWS.

Module 15: Visualizing and orchestrating big data

• Visualizing big data.
• Amazon QuickSight.
• Orchestrating a big data workflow.
• Hands-on lab 7: Using TIBCO Spotfire to visualize data.

Module 16: Big data design patterns

• Common architectures.

Module 17: Course wrap-up

• What’s next?.

In this course, you will learn how to:

• Recognize terminology and concepts as they relate to the AWS platform and navigate the AWS
  Management Console.
• Understand the foundational services, including Amazon Elastic Compute Cloud (EC2), Amazon Virtual
  Private Cloud (VPC), Amazon Simple Storage Service (S3), and Amazon Elastic Block Store (EBS).
• Understand the security measures AWS provides and key concepts of AWS Identity and Access
  Management (IAM).
• Understand AWS database services, including Amazon DynamoDB and Amazon Relational Database
  Service (RDS).
• Understand AWS management tools, including Auto Scaling, Amazon CloudWatch, Elastic Load
  Balancing (ELB), and AWS Trusted Advisor.

Intended audience:
This course is intended for:

• Individuals responsible for articulating the technical benefits of AWS services.
• Individuals interested in learning how to get started with using AWS.
• SysOps Administrators, Solution Architects and Developers interested in using AWS services.

Prerequisites:
We recommend that attendees of this course have:

• Working knowledge of distributed systems.
• Familiarity with general networking concepts.
• Working knowledge of multi-tier architectures.
• Familiarity with cloud computing concepts.

Day One:
Module 1: AWS introduction and history

• AWS Global Infrastructure.
• Demonstration: AWS Management Console.

Module 2: Compute, network and storage services

Module 3: AWS Security, AWS Identity and Access Management (IAM)

Module 4: Databases (RDS, DynamoDB)

Module 5: AWS elasticity and management tools

In this course, you will learn how to::

• Define what the cloud is and how it works.
• Differentiate between cloud computing and deployment models.
• Describe the AWS Cloud value proposition.
• Describe the basic global infrastructure of the cloud.
• Compare the different methods of interacting with AWS.
• Describe and differentiate between AWS service domains.
• Given a scenario, identify an appropriate solution using AWS Cloud services.
• Describe the Well-Architected Framework.
• Describe basic AWS Cloud architectural principles.
• Explain the Shared Responsibility model.
• Describe security services with the AWS Cloud.
• Define the billing, account management, and pricing models for the AWS platform.

Intended audience:
This course is intended for:

• Sales and Marketing.
• Legal.
• Business Analysts.
• Project Managers.
• AWS Academy students.
• IT professionals.

Prerequisites:

• There are no prerequisites for this course.

Day One
Module 1: Introduction to the AWS Cloud

• What is the AWS Cloud?.
• What are the benefits of the AWS Cloud?.
• AWS global infrastructure.
• AWS management interfaces.

Module 2: Getting started with the cloud

• Get started with AWS services.
• Build your infrastructure.
• Store your data.
• Secure your data.
• Hands-on lab: Amazon Simple Storage Service.

Module 3: Building in the cloud

• Go beyond servers and storage.
• Monitor AWS resources.
• Manage demand efficiently.
• Deploy database services.
• Automate deployment.
• Connect and share data.
• Deliver content faster.
• Hands-on lab: Build a Web Server on Amazon EC2.

Module 4: Security

• Secure your infrastructure.
• Manage authentication and authorization.
• Assess your security and compliance.
• Protect your infrastructure from Distributed Denial of Service (DDoS) attacks.
• AWS security compliance.

Module 5: Pricing models and cloud application support

• Fundamentals of pricing.
• Cost estimating tools.
• AWS Support.
• Hands-on lab: Auditing Security with AWS Trusted Advisor.

Module 6: Architecture

• The AWS Well-Architected Framework.
• Reference architectures.
• The future of the AWS Cloud.

This course is designed to teach you how to:

• Make architectural decisions based on AWS architectural principles and best practices.
• Leverage AWS services to make your infrastructure scalable, reliable, and highly available.
• Leverage AWS Managed Services to enable greater flexibility and resiliency in an infrastructure.
• Make an infrastructure based on AWS more efficient to increase performance and reduce costs.
• Use the Well-Architected Framework to improve architectures with AWS solutions.

Intended audience:
This course is intended for:

• Solutions Architects.
• Solution Design Engineers.
• Anyone who needs to understand the scope of cloud architectures.

Prerequisites:
We recommend that attendees of this course have:

• Taken the AWS Cloud Practitioner Essentials classroom or digital training.
• Working knowledge of distributed systems and multi-tier architectures.
• Familiarity with general networking and cloud computing concepts.

Day One
Module 1: Introduction:

• The Well-Architected Framework.
• AWS Global Infrastructure.

Module 2: The simplest architectures:

• Amazon Simple Storage Service (Amazon S3).
• Amazon S3 Glacier.
• Choosing AWS Regions for your architectures.
• Hands-on lab: Hosting a Static Website.

Module 3: Adding a compute layer:

• Amazon Elastic Compute Cloud (Amazon EC2).
• Amazon Machine Images (AMIs).
• Amazon Elastic Block Storage (Amazon EBS).
• Amazon Elastic File System (Amazon EFS).
• Amazon FSx.

Module 4: Adding a database layer:

• Database layer considerations.
• Amazon Relational Database Service (Amazon RDS).
• Amazon DynamoDB.
• AWS Database Migration Service (AWS DMS).
• Hands-on lab: Deploying a Web Application on AWS.

Module 5: Networking in AWS – Part 1:

• Amazon Virtual Private Cloud (Amazon VPC).
• Network security in the cloud.
• Hands-on lab: Creating a VPC.

Day Two:
Module 6: Networking in AWS – Part 2:

• AWS VPN connections.
• AWS Direct Connect (DX).
• VPC peering.
• AWS Transit Gateway.
• Load balancing on AWS.
• Amazon Route 53.

Module 7: AWS Identity and Access Management (IAM):

• Account users and AWS IAM.
• Federating users.
• Amazon Cognito.
• AWS Organizations.

Module 8: Elasticity, high availability, and monitoring:

• Amazon CloudWatch.
• AWS CloudTrail.
• Amazon EC2 Auto Scaling.
• Scaling your databases.
• Hands-on lab: Creating a highly available environment.

Module 9: Automation:

• AWS CloudFormation.
• AWS Systems Manager.
• AWS OpsWorks.
• AWS Elastic Beanstalk.
• Hands-on lab: Automating infrastructure deployment with AWS CloudFormation.

Day Three:
Module 10: Caching:

• Caching on AWS with Amazon CloudFront.
• Session management.
• Amazon DynamoDB Accelerator (DAX).
• Amazon ElastiCache.

Module 11: Building decoupled architectures:

• Amazon Simple Queue Service (Amazon SQS).
• Amazon Simple Notification Service (Amazon SNS).

Module 12: Microservices and serverless architectures:

• mazon Elastic Container Service (Amazon ECS).
• AWS Fargate.
• AWS Lambda.
• Amazon API Gateway.
• AWS Step Functions.
• Hands-on lab: Implementing a serverless architecture with AWS Managed Services.

Module 13: RTO/RPO and backup recovery setup:

• Disaster planning.
• Data replication.
• Recovery strategies.
• AWS Storage Gateway.

Module 14: Optimization and review:

• Best practices for optimization.
• Review questions.

In this course, you will learn how to:

• Make architectural decisions based on AWS architectural principles and best practices.
• Use AWS services to make your infrastructure scalable, reliable, and highly available.
• Use AWS Managed Services to enable greater flexibility and resiliency in an infrastructure.
• Make an AWS-based infrastructure more efficient to increase performance and reduce costs.
• Use the Well-Architected Framework to improve architectures with AWS solutions.

Intended audience:
This course is intended for:

• Solutions Architects who are new to designing and building cloud architectures.
• Data Center Architects who are migrating from on-premises environment to cloud architectures.
• Other IT/cloud roles who want to understand how to design and build cloud architectures.

Prerequisites:
We recommend that attendees of this course have:

• Attended AWS Technical Essentials classroom training or have equivalent experience.
• Working knowledge of distributed systems.
• Familiarity with general networking concepts.
• Working knowledge of multi-tier architectures.
• Familiarity with cloud computing concepts.

Day One
Module 1: Introduction:

• The real story of AWS.
• Well-Architected Framework.
• Six advantages of the cloud.
• Global infrastructure.

Module 2: The Simplest Architectures:

• S3.
• Glacier.
• Choosing your regions.
• Hands-on lab: Static Website.

Module 3: Adding a Compute Layer:

• EC2.
• Storage solutions for instances.
• Purchasing options such as dedicated host vs instances.

Module 4: Adding a Database Layer:

• Relational vs non-relational.
• Managed databases.
• RDS.
• Dynamo DB.
• Neptune.
• Hands-on lab: Deploying a web application on AWS.

Module 5: Networking in AWS Part 1:

• VPC.
• CIDR and subnets.
• Public vs private subnets.
• NAT and internet gateway.
• Security groups.

Day Two:
Module 6: Networking in AWS Part 2:

• Virtual Private Gateway.
• VPN.
• Direct Connect.
• VPC peering.
• Transit Gateway.
• VPC Endpoints.
• Elastic Load Balancer.
• Route 53.
• Hands-on lab: Creating a VPC.

AWS Identity and Access Management (IAM):

• IAM.
• Identity federation.
• Cognito.

Module 8: Organizations:

• Organizations.
• Multiple account management.
• Tagging strategies.

Module 9: Elasticity, High Availability, and Monitoring:

• Elasticity vs inelasticity.
• Monitoring with CloudWatch, CloudTrail, and VPC Flow Logs.
• Auto scaling.
• Scaling databases.
• Hands-on lab: Creating a highly available environment.

Module 10: Automation:

• Why automate?.
• CloudFormation.
• AWS Quick Starts.
• AWS Systems Manager.
• AWS OpsWorks.
• AWS Elastic Beanstalk.

Day Three:
Module 11: Deployment Methods:

• Why use a deployment method?.
• Blue green and canary deployment.
• Tools to implement your deployment methods.
• CI/CD.
• Hands-on lab: Automating infrastructure deployment.

Module 12: Caching:

• When and why you should cache your data.
• Cloudfront.
• Elasticache (Redis/Memcached).
• DynamoDB Accelerator.

Module 13: Security of Your Data:

• Shared responsibility model.
• Data classification.
• Encryption.
• Automatic data security.

Module 14: Building Decoupled Architecture:

• Tight coupling vs loose coupling.
• SQS.
• SNS.

Module 15: Optimizations and Review:

• Review questions.
• Best practices.
• Activity: Design and architecture – two trues and one lie.

Day Four:
Module 16: Microservices:

• What is a microservice?.
• Containers.
• ECS.
• Fargate.
• EKS.

Module 17: Microservices:

• Why use serverless?.
• Lambda.
• API Gateway.
• AWS Step Functions.
• Hands-on lab: Implementing a serverless architecture with AWS Managed Services.

Module 18: Microservices:

• Using managed services greatly increases resiliency.
• Serverless for resiliency.
• Issues with microservices to be aware of.
• DDoS.
• Hands-on lab: Amazon CloudFront content delivery and automating WAF rules.

Module 19: Networking in AWS Part 3:

• Elastic Network Adapter.
• Maximum transmission units.
• Global Accelerator.
• Site to site VPN.
• Transit Gateway.

Module 20: Understanding Costs:

• Simple monthly calculator.
• Right sizing your instances.
• Price sensitive architecture examples.

Day Five:
Module 21: Migration Strategies:

• Cloud migration strategies.
• Planning.
• Migrating.
• Optimizing.
• Hands-on lab: Application deployment using AWS Fargate.

Module 22: RTO/RPO and Backup Recovery Setup:

• Disaster planning.
• Recovery options.

Module 23: Final Review :

• Architecting advice.
• Service use case questions.
• Example test questions.

This course is designed to teach you how to:

• Apply the AWS Well-Architected Framework.
• Manage multiple AWS accounts for your organization.
• Connect an on-premises data center to the AWS Cloud.
• Move data from an on-premises data center to AWS.
• Design large data stores for the AWS Cloud.
• Understand different architectural designs for scalability.
• Protect your infrastructure from a DDoS attack.
• Secure your data on AWS with encryption.
• Enhance the performance of your solutions.
• Select the most appropriate AWS deployment mechanism.

Intended audience:
This course is intended for:

• Solution Architects.

Prerequisites:
We recommend that attendees of this course have:

• • Attended Architecting on AWS classroom training.
  • Achieved the AWS Certified Solutions Architect – Associate certification.
  • Experience in designing scalable and elastic applications on the AWS platform
    .

• Multiple accounts.
• Multi-account patterns.
• License management.
• Manage security and costs with multiple accounts.
• AWS Organizations.
• AWS Directory Service.
• Hands-on lab: Multi-VPC connectivity using a VPN.

Module 2: Advanced Network Architectures:

• Improve VPC network connections.
• Enhance performance for HPC workloads.
• VPN connections over AWS.
• AWS Direct Connect.
• AWS Transit Gateway.
• Amazon Route 53.
• Exercise: Design a hybrid architecture.

Module 3: Deployment Management on AWS:

• Application lifecycle management.
• Application deployment using containers.
• AWS Elastic Beanstalk.
• AWS OpsWorks.
• AWS CloudFormation.

Day Two:
Module 4: Deployment Management on AWS:

• Optimize Amazon S3 storage.
• Amazon ElastiCache.
• AWS Snowball.
• AWS Storage Gateway.
• AWS DataSync.
• Backup and archival considerations.
• Database migration.
• Designing for big data with Amazon DynamoDB.
• Hands-on lab: Build a failover solution with Amazon Route 53 and Amazon RDS.

Module 5: Deployment Management on AWS:

• AWS Auto Scaling.
• Migrating over-provisioned resources.
• Blue-green deployments on AWS.
• Hands-on lab: Blue-green deployment with AWS Elastic Beanstalk.

Day Three:
Module 6: Building resilient architectures:

• DDoS attack overview.
• AWS Shield.
• AWS WAF.
• Amazon GuardDuty.
• High availability using Microsoft SQL Server and Microsoft SharePoint on AWS.
• High availability using MongoDB on Amazon EC2.
• AWS Global Accelerator.
• Hands-on lab: CloudFront content delivery and automating AWS WAF rules.

Module 7: Encryption and data security:

• Encryption primer.
• DIY key management in AWS.
• AWS Marketplace for encryption products.
• AWS Key Management Service (AWS KMS).
• Cloud Hardware Security Module (HSM).
• Comparison of key management options.
• Hands-on lab: AWS KMS with envelope encryption.

In this course, you will learn to:

• • Select and justify the appropriate ML approach for a given business problem.

• Train, evaluate, deploy, and tune an ML model using Amazon SageMaker.
• Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in
  AWS.
• Apply machine learning to a real-life business problem after the course is complete.

Intended audience:
This course is intended for:

• Developers.
• Solutions Architects.
• Data Engineers.
• Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon
  SageMaker.

Prerequisites:

We recommend that attendees of this course have:

• Basic knowledge of Python programming language.
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch).
• Basic experience working in a Jupyter notebook environment.

Day One: 

Module 0: Introduction:
• Pre-assessment

Module 1: Introduction to Machine Learning and the ML Pipeline:
• Overview of machine learning, including use cases, types of machine learning, and key concepts
• Overview of the ML pipeline.
• Introduction to course projects and approach.

Module 2: Introduction to Amazon SageMaker:
• Introduction to Amazon SageMaker.
• Demo: Amazon SageMaker and Jupyter notebooks.
• Hands-on: Amazon SageMaker and Jupyter notebooks.

Module 3: Problem Formulation:
• Overview of problem formulation and deciding if ML is the right solution.
• Converting a business problem into an ML problem.
• Demo: Amazon SageMaker Ground Truth.
• Hands-on: Amazon SageMaker Ground Truth.
• Practice problem formulation.
• Formulate problems for projects.

Day Two:
Checkpoint 1 and Answer Review

Module 4: Preprocessing:
• Overview of data collection and integration, and techniques for data preprocessing and
visualization.
• Practice preprocessing.
• Preprocess project data.
• Class discussion about projects.

Day Three:
Checkpoint 2 and Answer Review.

Module 5: Model Training:
• Choosing the right algorithm.
• Formatting and splitting your data for training.
• Loss functions and gradient descent for improving your model.
• Demo: Create a training job in Amazon SageMaker.

Module 6: Model Evaluation:
• How to evaluate classification models.
• How to evaluate regression models.
• Practice model training and evaluation.
• Train and evaluate project models.
• Initial project presentations.

Day Four:
Checkpoint 3 and Answer Review.

Module 7: Feature Engineering and Model Tuning:
• Feature extraction, selection, creation, and transformation.
• Hyperparameter tuning.
• Demo: SageMaker hyperparameter optimization.
• Practice feature engineering and model tuning.
• Apply feature engineering and model tuning to projects.
• Final project presentations.

Module 8: Deployment:
• How to deploy, inference, and monitor your model on Amazon SageMaker.
• Deploying ML at the edge.
• Demo: Creating an Amazon SageMaker endpoint.
• Post-assessment.
• Course wrap-up.

Introduction to Node.js

Learning Objective: In this best Node JS certification module, you will learn what is Node.js and what makes Node.js so popular. You will also learn how to use Node Package Manager (NPM) and Nodemon.

Topics:

• What is Node.js?.
• Why Node.js?.
• Installing NodeJS.
• Node in-built packages (buffer, fs, http, os, path, util, url).
• Node.js Modules.
• Import your own Package.
• Node Package Manager (NPM).
• Local and Global Packages.
• Push code to GitHub.

Hands On:

• Installation of Node.js and Visual Studio Code.
• Installation of GIT and push codes to GIT repository.

File System Module and Express.js

Learning Objective: In this Node JS online course module, you will learn how to get user inputs via Command Line Arguments and store data using File system. You will also learn how to create the applications using Express Framework, whereas manage and deploy them using PM2 and Nginx.

Topics:

• Get Input from Users.
• Pass Multiple Arguments with Yargs.
• File System Module.
• Operations associated with File System Module.
• JSON Data.
• Http Server and Client.
• Sending and receiving events with EventEmitters.
• Express Framework.
• Run a Web Server using Express Framework.
• Routes.
• Deploy application using PM2 and Nginx.

Hands On:

• Build an API using express, read file with FS module, and deploy application using PM2 and
• Nginx.

Asynchronous Programming :

Learning Objective: In this Node JS certification module, you will learn how to develop asynchronous Node.js applications using Call stack, Callback queue and Event Loop mechanism. You will also gain knowledge on how to work with HTTP request, Promises and EJS templates.

Topics:

• Call Stack.
• Callbacks, Callback Queue and Event Loop.
• Callback Abstraction.
• Callback Chaining.
• Promises.
• Promise Chaining.
• Request Package.
• Customizing HTTP Requests.
• Error handing with appropriate HTTP codes.
• Introduction to template engine (EJS).

Hands On:

• Create A HTML Page Using EJS Template.
• Create A Weather Application.

Integration with MongoDB and Email Servers:

Learning Objective: In this module, you will learn how to work with NoSQL Database – MongoDB. Also, you will learn how to send emails via Node.js application. 

Topics:

• Introduction to NoSQL Databases and MongoDB.
• Installation of MongoDB on Windows.
• Installation of Database GUI Viewer.
• Inserting Documents.
• Querying, Updating and Deleting Documents.
• Connect MongoDB and Node.js Application.
• Exploring SendGrid.
• Sending emails through Node.js application using SendGrid.

Hands On:

• Installation of MongoDB and MongoDB Compass.
• Connecting MongoDB And Node.js.
• Sending Email Via Node.js Application Using SendGrid.

REST APIs and GraphQL:

Learning Objective: In this module, you will learn how to run queries via Node.js application along with some important aspects of Node.js like REST APIs, setting up Mongoose, Postman Installation and GraphQL.

Topics:

• REST API.
• REST API in Express.
• Postman.
• MongoDB Driver API.
• Express Router.
• Mongoose API.
• GraphQL.
• GraphQL Playground.

Hands On:

• Working Of mongodb API.
• Working With Mongoose API.
• Working With GraphQL API.

User Authentication and Application Security:

Learning Objective: In this module, you will learn how to secure your application by implementing API Authentication and User authentication using JSON Web Tokens (JWT).Optional and default parameter.   

Topics:

• Authentication.
• Types of Authentication.
• Session Vs Tokens.
• JSON Web Tokens.
• Bcrypt.
• Node-localstorage.

Hands On:

• Build a Login Application

Building Node.js Applications using ES6:   

Learning Objective: In this module, you will learn how to write your application using ES6. Also, you will learn how to Design Dashboard and perform CRUD operations.

Topics:

• ES6 variables.
• Functions with ES6.
• Import and Export withES6.
• Async/Await.
• Introduction to Babel.
• Rest API with ES6.
• Browsing HTTP Requests with Fetch.
• Processing Query String.
• Creating API using ES6.
• Transpilation.
• Building Dashboard API.
• Creating dashboard UI with EJS.
• ES6 Aside: Default Function Parameters.
• Data Validation and Sanitization.

Hands On:

• Build a Dashboard Application Using ES6 concepts.

Dynamic Client-Server Interaction using Socket.IO:   

Learning Objective: In this module, you will learn how Build Realtime Chat Application using Socket.IO.

Topics:

• Web Sockets.
• Web Sockets.
• Socket.io.
• Broadcasting Events.
• Sharing Your Location.
• Event Acknowledgements.
• Form and Button States.
• Rendering Messages.
• Working with Time and Timestamps for determining Location of Messages.
• Storing Users, Rendering User List, Tracking Users Joining and Leaving.
• Deploying the Chat Application.
• Redis – Building API with Redis.

Hands On:

• Creating a Realtime Chat Application using Socket.io
• Build an API using Redis

Testing Node.js Applications:  

Learning Objective: In this module, you will learn how to test your Node.js applications using Mocha and Chai, along with some important testing concepts related to Node.js Application.

Topics:

• Writing Tests and Assertions.
• Testing Asynchronous Code.
• Testing an Express Application.
• Setup and Teardown.
• Testing with Authentication.
• Advanced Assertions.
• Mocking Libraries.
• Wrapping up User Tests.
• Setup Task Test Suite.
• Testing with Task Data.

Hands On:

• Perform Testing with Mocha and Chai.

Microservices Application:

Learning Objective: In this module, you will learn how to build a Node.js application based on Microservices architecture and also how to deploy them to Docker containers. 

Topics:

• Why Microservices?.
• What is Microservices?.
• Why Docker?.
• JSX.
• What is Docker?.
• Terminologies in Docker.
• Child Processes.
• Types of child process.

Hands On:

• Microservices with Docker.
• Working of Child Processes. 

Goal: In this module, you will understand the setup and configuration of MongoDB® High Availability, Disaster Recovery, and Load Balancing.

Skills:

• Create, deploy, and manage Replica sets.
• Create and administer Sharded clusters.
• Perform Data Partitioning with chunks.

Objectives:

After completing this module, you should be able to:

• Understand the concepts of replica set.
• Understand the concept of sharing in MongoDB®.
• Create a production like Sharded cluster.

Topics:

• Introduction to Replication (High Availability)
• Concepts around Replication
• What is Replica Set and Master Slave Replication?
• Type of Replication in MongoDB
• How to setup a replicated cluster & managing replica sets etc.,
• Introduction to Sharding (Horizontal Scaling),
• Concepts around Sharding, what is shards, Key,
• Config Server, Query Router etc.
• How to setup a Sharding,
• Type of Sharding (Hash Based, Range Based etc.), and Managing Shards.

Hands-on/Demo/Use-case:

• Replication setup
• Set up Sharding

 
7. MongoDB Security

Goal: In this module, you will learn security related with MongoDB, Integration with various tools and technology. Also, you will learn to integrate it with various reporting and Analytical tools like Pentaho, Jaspersoft etc.

Skills:

• Setup authentication and encryption.
• Integrate MongoDB with various tools and applications.

Objectives:

After completing this module, you should be able to:

• Know security concepts in MongoDB®.
• Understand how Authentication and Authorization works.
• Integrate MongoDB® with Java.
• Integrate MongoDB® with Hadoop, Hive, & Pentaho.
• Integrate MongoDB® with Jaspersoft & Robomongo.

Topics:

• Security Introduction.
• Security Concepts.
• Integration of MongoDB with Jaspersoft.
• Integration of MongoDB with Pentaho.
• Integration of MongoDB with Hadoop/Hive.
• Integration of MongoDB with Java.
• Integration of MongoDB with GUI Tool Robomongo.
• Case Study MongoDB and Java.

Hands-on/Demo/Use-case:

• Integration Scenarios.

 
8. Application Engineering and MongoDB Tools:

Goal: In this module, you will learn MongoDB® tools to develop and deploy your applications. This

module will also help you understand the multiple package components and advance concepts related to MongoDB integration, Hadoop and MongoDB integration.

Skills:

• Perform MongoDB packaging.
• Setup limits and thresholds.
• Integrate with R.

Objectives:

After completing this module, you should be able to:

• Use MongoDB® tools.
• Understand Jaspersoft integration with MongoDB®.
• Know other popular use case of MongoDB®.
• Know various API drivers available for MongoDB®.
• Run few query operators through http interface.
• Topics.
• MongoDB Package Components.
• Configuration File Options.
• MongoDB Limits and Thresholds.
• Connection String URI Format/ Integration of any compatible tool with MongoDB API and Drivers for.
• MongoDB.
• MMS (MongoDB Monitoring Service).
• HTTP and Rest Interface.
• Integration of MongoDB with Hadoop and Data Migration MongoDB with Hadoop (MongoDB to Hive).
• Integration with R.

Hands-on/Demo/Use-case:

• MongoDB with R integration

 
9. MongoDB on the Cloud:

Goal: In this module, you will learn about various cloud products offered by MongoDB and how they

can be used to host or manage your MongoDB deployments.

Skills:

• Know MongoDB Cloud products.
• Use Cloud products in MongoDB deployments.

Objectives:

After completing this module, you should be able to:

• Understand MongoDB Stitch.
• Learn MongoDB Atlas.
• Explore MongoDB Cloud Manager.
• Setup MongoDB Ops Manager.

Topics:

• Overview of MongoDB Cloud products.
• Using Cloud Manager to monitor MongoDB deployments.
• Introduction to MongoDB Stitch.
• MongoDB Cloud Atlas.
• MongoDB Cloud Manager.
• Working with MongoDB Ops Manager.

Hands-on/Demo/Use-case:

• MongoDB Cloud Manager – exploring.

10. Diagnostics and Fixes:

Goal: In this module, you will learn some of the common real-time scenarios you might find in

production and how they can be fixed, once identified.

Skills:

• Troubleshoot slow queries.
• Diagnose connectivity problems.

Objectives:

After completing this module, you should be able to:

• Understand diagnostic tools.
• Learn common production issues.
• Learn fixes and solutions.

Topics:

• Overview of tools.
• MongoDB Diagnostic Tools.
• Diagnostics Commands.
• MongoDB Deployment.
• Setup & Configuration, Scalability, Management & Security.
• Slow Queries.
• Connectivity.

Hands-on/Demo/Use-case:

• Troubleshooting scenarios.

You will learn to create automatable and repeatable deployments of networks and systems on the AWS platform. This course covers AWS features and tools related to configuration and deployment, as well as common techniques used throughout the industry for configuring and deploying systems.

Module 1: AWS Overview:

Goal: In this module, you will you will learn compute services offered by AWS. We will cover different Amazon AMIs, a demo on launching an AWS EC2 instance, connect with an instance and hosting a website on AWS EC2 instance.

Objective:
At the end of this module, you should be able to:

• Understand Cloud Computing.
• Understand AWS Availability Zones and Regions.

Topics:

• Cloud Computing.
• Cloud Services.
• Amazon Web Services (AWS).
• AWS Global Infrastructure.

Hands-on: Signing Up for a Free Tier Account with AWS

Module 2: Computing on AWS
Goal: In this module, you will you will learn compute services offered by AWS. We will cover different Amazon AMIs, a demo on launching an AWS EC2 instance, connect with an instance and hosting a website on AWS EC2 instance.
At the end of this module, you should be able to:

• Understand Amazon Machine Image (AMI).
• Create, Manage and Troubleshoot an Instance.
• Understand Security Groups, Key Pairs, Tenancy – Shared vs Dedicated, IAM, CLI.
• Differentiate between Elastic IP & Public IP.
• Define Elastic Network Interfaces (ENI).

Topics:

• Introduction.
• Amazon Machine Image (AMI).
• What is an Instance?.
• Types of Instances.
• Creating Instances.
• Managing Instances.
• Troubleshooting Instances.
• Network Interfaces.
• Different Categories of IP.

Hands-on: Creating EC2 Instance .

Module 3: Storage and Archiving in AWS

Goal: In this module, you will learn about the different storage services offered by AWS and how they can be used to transfer the data.

Objective:
At the end of this module, you should be able to:

• Define Cloud Storage.
• Define Types of AWS Storage.
• Create, Manage and Expand Amazon Elastic Block Storage (EBS).
• Configure Access to Amazon S3 resources.
• Store and retrieve Amazon S3 objects.
• Use Amazon Glacier.

Topics: 

• Cloud Storage 
• Types of AWS Cloud Storage 
• Understanding AWS Storage 
• Amazon Elastic Block Storage (EBS) 
• Amazon S3 
• Amazon Glacier 
• Storage Security 
• Storage Gateway 
• Snowball 
• Troubleshooting Storage in AWS 
• Pricing in AWS 

Hands-on: 

• Restoring an EBS Volume from a Snapshot 
• Upload and download files from Amazon S3 

Module 4: Networking in the Cloud 

Goal: In this module, you will be introduced to Virtual Private Cloud (VPC), learn about the VPC Scenarios, VPC components, VPC Peering, Configuring Subnets, Network Address Translation (NAT) and Configuring AWS EC2 instance as NAT server. 

Objective: 

At the end of this module, you should be able to: 

• Understand Cloud Networking 
• Understand and Configure VPC 
• Create and Manage Networks in AWS 
• Configure Route Tables for NAT 
• Configure EC2 instance as NAT server 
• Manage Network Security 
• Troubleshoot Networks 

Topics: 

• Amazon VPC 
• VPC Subnets 
• Route Tables 
• Elastic IPs 
• Internet Gateway 
• NAT Gateway 
• Access Control Lists (ACLs) 
• Security Groups 
• Creating and Managing a VPC 
• Route53 
• Network Security 
• Troubleshooting Networks in AWS 

Hands-on: Building a VPC Environment 

Module 5: Database Services 

Goal: In this module, you will learn about the different database services offered by AWS to deal with structured and unstructured data 

Objective: 

At the end of this module, you should be able to: 

• Understand Different Databases services 

• Amazon RDS 
• Amazon DynamoDB 
• Amazon ElastiCache 

Topics: 

• Types of Database Instances 
• Amazon RDS 
• Amazon DynamoDB 
• Amazon ElastiCache 

Hands-on: 

• Creating a MySQL DB Instance via Relational Database Service 
• Creating a table and loading data in Amazon DynamoDB 

Module 6: Creating Scalable Deployments in AWS 

Goal: In this module, you will learn ‘scaling’ and ‘load distribution techniques’. Objectives: At the end of this module, you will be able to: 

• Understand Common Deployment Scenarios 
• Redirect Traffic with Elastic Load Balancing (ELB) 
• Scale to Demand with Auto Scaling 

Topics: 

• Scaling in the Cloud 
• Load Balancer 
• Elastic Load Balancing (ELB) 
• Auto Scaling 

Hands-on: Auto Scaling to scale up the number of servers available for a specific task. 

Module 7: Monitoring in AWS 

Goal: In this module, you will learn about the monitoring services offered by AWS. Setting up alerts and notifications for AWS resources and AWS usage billing with AWS CloudWatch. Also track user activity and API usage using CloudTrail 

Objective: 

At the end of this module, you will be able to: 

• Use CloudWatch 
• Use CloudTrail 
• Retrieve Instance log files with CloudWatch 

Topics: 

• Logging Basics 
• CloudWatch 
• CloudWatch logs 
• AWS CloudTrail 
• Monitoring and Security 
• IAM 
• Troubleshooting 

Hand-on: 

• Enabling CloudTrail Log Delivery to a S3 Bucket 
• Configuring CloudWatch Logs agent on an Instance 

Module 8: Managing Resource Consumption and Configuration Management in AWS 

Goal: In this module, you will learn techniques in AWS to manage resource consumption and using configuration management tools 

Objective: 

Upon completing this module, you will be able to: 

• Identify cost reduction opportunities 
• Use Tagging 
• Use AWS Trusted Advisor 
• Create and use AMIs 

Topics: 

• Identifying Cost Reduction Opportunities in the cloud 
• Tagging 
• Monitoring and Expenditure Alerting with Amazon CloudWatch 
• AWS Trusted Advisor 
• Creating Amazon Machine Images (AMI) 
• AWS OpsWorks 
• AWS UserData 
• AWS CloudFormation 
• Amazon Elastic BeanStalk 

Hands-on: Create a Stack using an AWS CloudFormation Template Certification Project To be decided.

• Introduction to Terraform course or intermediate understanding of Terraform.
• AWS fundamentals, including at least beginner knowledge of EKS.
• Understanding of Helm, at least beginner knowledge.

Objectives:

Upon completion of this course, you should be able to:

• Understand the different pieces of the Spinnaker application model and how they relate to automating deployments and/or resource management.
• Grasp continuous integration and delivery/deployment pipelines and how they relate to Spinnaker’s pipelines, stages, etc. for deploying applications to the cloud.
• Know the available provider integrations for Spinnaker, and capabilities for working on less standard, non-provider-based deployments and automated scenarios.
• Use Spinnaker to deploy a micro-service into a cloud Kubernetes environment such as EKS, GKE, etc.
• Understand how Spinnaker works with Helm for templating, artifact creation, and artifact (templated chart) installation and upgrades.
• Learn how to use Spinnaker in Falcon.

Section 1: Introduction

Continuous Integration and Delivery
Overview of Spinnaker

• Applications.
• Pipelines.
• Triggers.
• Clusters.
• Server Groups.
• Load Balancers.
• Firewalls.

Section 2: Spinnaker Architecture.

Section 3: Kubernetes Quick Ramp-up.

Section 4: Spinnaker with Jenkins.

• Packaging app in Jenkins and Deploying using Spinnaker.

Section 5: Spinnaker Accounts:

• Github.
• S3.
• ECR.

Section 6: Spinnaker Artifacts

• YAML files.
• S3 files.
• Docker Containers.

Lab 1: Creating an Application
Section: Pipelines

• Stages.
• Deploy Manifest Manual Judgment.
• Wait.
• Triggers.
• Github.
• ECR.
• Other Pipelines.

Lab 2: Deploying to Staging and Production.
Section: Spinnaker Expression Language.
Section: Diagnosing Errors & Introspecting the Run.
Section: Deployment Strategies.

• Highlander.
• Blue/Green.
• Canary.

Section: Rolling Back

• Ad Hoc Rollbacks.
• Programmatic Rollback.

Section: Scaling Resources
Section: Restricting Execution

Lab 4: Manual Judgments, Scaling, Rolling back

Section: Running Jobs

• Review of Kubernetes Jobs.
• Using a Kubernetes container to perform work.

Section: Running AWS EC2 and ECS2

• Cloud Providers.
• Designating a VPC.
• Baking EC2 instances.
• Running ECS with a Docker Image.
• Operations Available using AWS Cloud Provider.
• Compare and Contrast AWS with Kubernetes.

Course Introduction Module 1: SRE Anti-patterns:

• Rebranding Ops or DevOps or Dev as SRE.
• Users notice an issue before you do.
• Measuring until my Edge.
• False positives are worse than no alerts.
• Configuration management trap for snowflakes.
• The Dogpile: Mob incident response.
• Point fixing.
• Production Readiness Gatekeeper.
• Fail-Safe really?.

Module 2: SLO is a Proxy for Customer Happiness:

• Define SLIs that meaningfully measure the reliability of a service from a user’s perspective.
• Defining System boundaries in a distributed ecosystem for defining correct SLIs.
• Use error budgets to help your team have better discussions and make better data-driven decisions.
• Overall, Reliability is only as good as the weakest link on your service graph.
• Error thresholds when 3rd party services are used.

Module 3: Building Secure and Reliable Systems:

• SRE and their role in Building Secure and Reliable systems.
• Design for Changing Architecture.
• Fault tolerant Design.
• Design for Security.
• Design for Resiliency.
• Design for Scalability.
• Design for Performance.
• Design for Reliability.
• Ensuring Data Security and Privacy.

Module 4: Full-Stack Observability:

• Modern Apps are Complex & Unpredictable.
• Slow is the new down.
• Pillars of Observability.
• Implementing Synthetic and End user monitoring.
• Observability driven development.
• Distributed Tracing.
• What happens to Monitoring?.
• Instrumenting using Libraries an Agents.

Module 5: Platform Engineering and AIOPs:

• Taking a Platform Centric View solves Organisational scalability challenges such as fragmentation, inconsistency and unpredictability.
• How do you use AIOps to improve Resiliency.
• How can DataOps help you in the journey.
• A simple recipe to implement AIOps.
• Indicative measurement of AIOps.

Module 6: SRE & Incident Response Management:

• SRE Key Responsibilities towards incident response.
• DevOps & SRE and ITIL.
• OODA and SRE Incident Response.
• Closed Loop Remediation and the Advantages.
• Swarming – Food for Thought.
• AI/ML for better incident management.

Module 7: Chaos Engineering:

• Navigating Complexity.
• Chaos Engineering Defined.
• Quick Facts about Chaos Engineering.
• Chaos Monkey Origin Story.
• Who is adopting Chaos Engineering.
• Myths of Chaos.
• Chaos Engineering Experiments.
• GameDay Exercises.
• Security Chaos Engineering.
• Chaos Engineering Resources.

Module 8: SRE is the Purest form of DevOps:

• Key Principles of SRE.
• SREs help increase Reliability across the product spectrum.
• Metrics for Success.
• Selection of Target areas.
• SRE Execution Model.
• Culture and Behavioral Skills are key.
• SRE Case study.

Post-class assignments/exercises:

• Non-abstract Large Scale Design (after Day 1).
• Observability and Monitoring (after Day 2).
• Chaos Engineering Instrumentation.

Course Introduction:

Module 1: Exploring Observability Module.

2: Pillars of Observability.

Module 3: Open Source Landscape for Observability.

Module 4: Service Maps and Topology.
Module 5: DataOps Helps Get Observability Right.

Module 6: Building Observability with AIOps.

Module 7: Security and Networking with Observability.

Module 8: Observability Practices for DevOps and SRE.

Course Outline:

Module 1: Realizing DevSecOps Outcomes:

• Origins of DevOps.
• Evolution of DevSecOps.
• CALMS.
• The Three Ways.

Module 2: Defining the Cyberthreat Landscape:

• What is the Cyber Threat Landscape?.
• What is the threat?.
• What do we protect from?.
• What do we protect, and why?.
• How do I talk to security?.

Module 3: Building a Responsive DevSecOps Model:

• Demonstrate Model.
• Technical, business and human outcomes.
• What’s being measured?.
• Gating and thresholding.

Module 4: Integrating DevSecOps Stakeholders:

• The DevSecOps State of Mind.
• The DevSecOps Stakeholders.
• What’s at stake for who?.
• Participating in the DevSecOps model.

Module 5: Establishing DevSecOps Best Practices:

• Start where you are.
• Integrating people, process and technology and governance.
• DevSecOps operating model.
• Communication practices and boundaries.
• Focusing on outcomes.

Module 6: Best Practices to get Started:

• The Three Ways.
• Identifying target states.
• Value stream-centric thinking.

Module 7: DevOps Pipelines and Continuous Compliance:

• The goal of a DevOps pipeline.
• Why continuous compliance is important.
• Archetypes and reference architectures.
• Coordinating DevOps Pipeline construction.
• DevSecOps tool categories, types and examples.

Module 8: Learning Using Outcomes:

• Security Training Options.
• Training as Policy.
• Experiential Learning.
• Cross-Skilling.
• The DevSecOps Collective Body of Knowledge.
• Preparing for the DevSecOps Foundation certification exam.

Course Introduction:

Module 1: DevSecOps Advanced Basics

• Why Advance Practices?.
• General Awareness.
• People-Finding Them.
• Core Process.
• Technology Overview.

Module 2: Understanding Applied Metrics

• Metric Terms.
• Accelerating.
• People-Reporting and Recording.
• Integrating Process.
• Technology Automation.

Module 3: Architecting and Planning for DevSecOps

• Architecture Basics.
• Finding an Architect.
• Reporting and Recording.
• Environments Process.
• Accelerating Decisions.

Module 4: Creating a DevSecOps Infrastructure

• What is Infrastructure?.
• Equipping the Team.
• Design Challenges.
• Monitoring Infrastructure.

Module 5: Establishing a Pipeline

• Pipelines and Workflows.
• Engineers and Capabilities.
• Continuous Engagement.
• Automate and Identify.

Module 6: Observing DevSecOps Outcomes

• Observability vs. Monitoring.
• Who gets which Report?.
• Setting Observation Points.
• Implementing Observability.

Module 7: Practical 3rd Way Applications

• Revisiting 3rd Way.
• Building Experiments.
• Getting the Most from the Experiment.

Module 8: The Future of DevOps

• Looking Towards the Future.
• Staying Trained.
• Innovation.
• What, and from Who?.

Post-Class Assignments/Exercises

• Extended advanced reading associated with Case Stories from the course.

Course Material

Suresh GP, M-Tech is currently the Founder & Managing Director of TaUB Solutions. He has more than 20+ Years of IT experience and specialized in IT Service Management, IT Governance, Agile, DevOps and Business Relationship Management. Suresh as a Principal Consultant does Consulting, Training, Simulations and Implementation services for DevOps to Fortune 2000 organization.
He was also awarded top 25 thought leaders in service management for 2017 by HDI.
East at BRM Institute. To complement his extensive experience, he has earned wide range of certifications that include ITIL V3 Expert, ITIL 4, DevOps Leader, CGEIT, CBRM, CASM, PMP, ISO 20000 Practitioner and ISO/IEC 20000 & 27001 Lead Auditor. He is a regular blogger and speaker in National & International Forums like itSMF, DevOps Institute, PMI, ISACA. He was awarded itSMF Contributor Award by itSMF Singapore in 2013 and 4 BRM Excellence Awards at BRM Connect 2017.

Upon completing this course, delegates will be able to:

• Have a better understanding of current widespread cyber attacks.
• Identify social engineering attacks.
• Understand security risks involving personal equipment.
• Understand the risk of unpatched services.
• Efficiently create security policies.
• Improve existing security policies.

Who Can attend this course:

• The course is meant for high-level executive managers.

Pre Requisite:

• Basic computer skills.
• Basic+ knowledge of English.

Module 01 : Overview

Attack vectors:

• DNS Spoof.
• DDOS. 4
• Brute-Force. 4
• Others. 4

Module 02: Social Engineering Methods and Security Awareness

Phishing:

• Email security.
• Suspicious emails or addresses.
• Suspicious file extensions.
• Suspicious links.
• Awareness of phishing emails.

Passwords:

• Strength and length.
• Never save passwords as cleartext.
• Use password complexity.
• Password change rate.
• Never keep a physical copy of the passphrases.
• Usage of unique passwords.
• Never use old passwords or the same password for different applications.
• Password security solutions.
• Two-factor importance & methodology.

Personal Equipment – BYOD:

• Restriction of personal drive usage.
• Restriction of personal laptops.
• Restriction of copies or backups of.
• Company information or software.

Module 03: Defining Security Policies

• Security Strategy Planning.
• Security Policy Scoping.
• How to create a backup plan.
• How to create a Disaster Recovery Plan.
• Creating an organizational security policy.
• Compliances & regulations.
• How to follow ISO standard

Module 04: Open-Source Intelligence (OSINT)

• The importance of gathering information.
• Data types that can expose organizations.
• Open-source intelligence terminology & definitions.
• Darknet.
• Types of OSINT sources:
  • Google dorks for OSINT.
  • Maltego (automation).
  • Shodan.

Module 05: Risk Assessment & management

• Threat modeling.
• Case studies of policy.
• Protection control types.
• Computer laws and crimes.
• Audit and assessment.
• Risk management.
• Risk evaluation.
• Risk response.
• Cyber in India – CRT Team Management.

• No prior experience in AWS required
• Experience in Virtualized environments preferable
• Experience in Linux command-line preferable
• Bring-your- own-device (BYOD) for hands-on exercises on AWS. If you don’t have a laptop, you can tag along with the other participants
• You also need to create your AWS account to access 1-year free services and for that you need credit card

• Foundation – Concept of Cloud Computing, Virtualization
• Introduction to AWS concepts, Global Infrastructure, Regions, Availability Zones
• AWS Account Basics – setting up basic account and how to use it
• Overview of Compute, Storage and Networking on AWS
• Identity & Access Management
• Networking – VPC, Subnets and Security Groups
• Compute – EC2, ELB and Auto scaling
• Sample app architecture and deployment strategy
• Storage – S3, EBS and EFS
• Storage – RDS and Dynamo DB
• Monitoring – Cloud watch
• Cloud Front, Simple Email Service
• AWS Security Management
• Route 53
• DevOps and AWS
• Serverless architecture – AWS Lambda
• Microservices through AWS Lambda and AWS API gateway

• Manage Azure identities and governance (20–25%).
• Implement and manage storage (15–20%).
• Deploy and manage Azure compute resources (20–25%).
• Implement and manage virtual networking (15–20%).
• Monitor and maintain Azure resources (10–15%).

The course is highly recommended for individuals keen to leverage the cloud platform for enterprise success. Anyone who desires to manage Microsoft Azure will nd the course immensely helpful in preparing for the Microsoft Azure Administrator certication.

Module 1: Architecting Fundamentals.

Module 2: Account Security

Module 3: Networking in AWS Part-1.

Module 4: Compute Service.

Module 5: Storage Service.

Module 6: Database Service.

Module 7: Monitoring and Scaling.

Module 8: Automation.

Module 9: Containers.

Module 10: Networking in AWS.

Module 11: Serverless.

Module 12: Edge Services.

Module 13: Backup and Recovery.

Module 14: Course review and summary.

This course is intended for:

• Entry level professionals to build ICT skills through broad based training and certification
  in the ‘Cloud Computing’ sector.
• Solution Architects or Solution Design Engineers who are relatively new to AWS and
  want to fast track their AWS Certification.
• Solution Architects or Solution Design Engineers who are preparing to take the AWS
  Certified Solutions Architect – Associate exam.

QAI Cloud Computing Training program is for everyone playing a role or having an interest in the use and management of internet based services. Specific roles that would benefit are:

• Management Positions (Commercial, IT, Procurement, Finance, etc.).
• IT Professionals (Administrators, Service Managers).
• Staff of Service Providers.

Context

Cloud Computing ties in well with programs, such as IT Service Management and Information Security. The Cloud Computing certification has been created in close

Collaboration with industry and trade organizations.

Course Day wise Breakup

Day 1

The principles of Cloud computing
1.1 The concept of Cloud computing
1.2 The evolution of Cloud computing
1.3 Cloud computing architectures- Public, Private, Hybrid
1.4 Benefits and limitations of Cloud computing

2. Using the Cloud
2.1 Accessing the Cloud
2.2 Mobility and the Cloud

3. Security and Identity management
3.1 Securing in the Cloud
3.2 Identity Management

Day 2

Implementing and managing Cloud computing
4.1 Building local Cloud networks
4.2 Supporting the use of Cloud computing
4.3 Standards in Cloud computing

5. Evaluation of Cloud computing
5.1 The business case
5.2 Evaluating implementations

6.1: Mock Exam

6.2: Exin Cloud Computing Exam

Cloud Computing Certification

Exam content

Candidates are tested on five categories:

• The principles of Cloud Computing. This chapter deals with definitions, types of clouds (Public, Private and Hybrid) and cloud services (IAAS, PAAS, SAAS);
• Using the Cloud. This part is about accessing the cloud and mobility in the cloud ;
• Security and Compliance. Is about the risks of cloud computing and the measures you can take;
• Implementing and managing Cloud Computing. You learn about local cloud networks and how to support the use of cloud computing,
• Evaluation of Cloud Computing. Examples of the subjects here are cost aspects, (dis)advantages and SLA’s.

Exam Details

• Number of questions: 40
• Pass mark: 65% (26 out of 40)
• Open book/notes: no
• Electronic equipment permitted: no