Course Overview

Create and configure production-grade ROSA clusters as part of a larger AWS customer’s footprint and then integrate applications on ROSA with AWS services while keeping a good security posture.

Deploying Production AWS ROSA Clusters: Creation, Configuration, and Application Integration (CS229) teaches how to configure ROSA clusters as part of pre-existing AWS environments and how to integrate ROSA with AWS services commonly used by IT operations teams, such as Amazon CloudWatch. This course also teaches how to integrate applications deployed on ROSA with AWS services in a way that cluster administrators and platform engineers retain control of credentials and roles required by applications to access AWS services instead of exposing those credentials to application developers.

Note: This course is offered as a 4 day in person class or a 5 day virtual class. Durations may vary based on the delivery. For full course details, scheduling, and pricing, select your location then “get started” on the right hand menu.

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Course Objectives

• Create ROSA STS PrivateLink clusters
• Connect PrivateLink ROSA clusters to existing VPCs and enable administrators and developers to access those clusters
• Configure dedicated machine pools and node/pod autoscaling
• Configure node, cluster, and audit log forwarding to Amazon CloudWatch
• Configure authentication and group sync with Amazon Cognito
• Integrate with external container registries such as ECR and Quay.io to deploy applications from private image repositories
• Configure storage classes to enable application access to different EBS volume types
• Configure storage classes and security contexts to enable application access to shared EFS storage volumes
• Configure pod identity using STS/IRSA to enable application access to AWS services such as database (Aurora), integration (SQS), and object storage (S3)
• Provision AWS services for applications using the AWS Controllers for Kubernetes (ACK)
• Federate and query application metrics (application workload monitoring) with Amazon Managed Prometheus Service
• Aggregate and query structured application logs with Amazon CloudWatch
• Configure custom domains and TLS certificates for secure public access to applications

Course Content

PrivateLink Red Hat OpenShift on AWS (ROSA) Clusters

Create a PrivateLink ROSA cluster with STS and enable developers or administrators to access the API and router endpoints of the cluster.

Node and Pod Autoscaling

Configure a ROSA cluster and a workload to dynamically scale the number of cluster nodes and application pods according to load.

Integrate ROSA Clusters with Amazon Web Services

Configure ROSA clusters to forward logs to Amazon CloudWatch for long-term storage, aggregation, and analysis, and to authenticate OpenShift users by using Amazon Cognito.

Deploy Applications From External Registries

Deploy applications on Red Hat OpenShift Service on AWS (ROSA) from private container image repositories in external centralized container image registries.

Provide Amazon Storage Volumes for Applications

Configure Amazon Elastic Block Storage (EBS) or Amazon Elastic File System (EFS) volumes that meet the cost, performance, and sharing requirements of their applications.

Configure Application Access to AWS Services

Configure applications for access to shared AWS services by using Kubernetes service accounts, and provision dedicated AWS services by using Kubernetes custom resources.

OpenShift and AWS Application Observability

Configure ROSA clusters to forward application logs to Amazon CloudWatch and application metrics to Amazon Managed Service for Prometheus.

Custom Domains for ROSA Applications

Expose applications to internet users with secure URLs by using human-readable DNS domains.

Course Overview

Develop the skills necessary to configure a secure deployment solution for cloud-native apps. Learn how to build, deploy, scale, and manage containerized cloud-native apps using Azure Container Apps, Azure Container Registry, and Azure Pipelines.

Course Content

Module 1 Get started with cloud native apps and containerized deployments

This module provides an introduction to cloud-native applications, the benefits of containerized deployments, the options for containerized deployments on the Azure platform, and the features of Azure Container Apps.

Module 2 Configure Azure Container Registry for container app deployments

This module teaching users how to set up and configure an Azure Container Registry for deploying containerized applications to Azure Container Apps.

Module 3 Configure a container app in Azure Container Apps

This module examines the features and capabilities of Azure Container Apps, and then focuses on how to create, configure, scale, and manage container apps using Azure Container Apps.

Module 4 Configure continuous deployment for container apps

This module explores deployment options for containerized apps. It reviews the features of Azure DevOps and examines automated deployments to Container Apps using Azure Pipelines.

Module 5 Scale and manage deployed container apps

This module reviews the concept of revisions in Azure Container Apps and examines options for application lifecycle management. It also examines options for scaling and traffic splitting using Azure Container Apps.

Module 6 Guided project – Deploy and manage a container app using Azure Container Apps

This module guides learners through the end-to-end process of building, deploying, and managing containerized applications using Azure Container Apps, Azure Container Registry, Azure Pipelines, and other tools and resources.

Course Overview

In this learning path, you practice deploying containers, container orchestration, and managing clusters on Azure Kubernetes Service. The skills validated include deploying, configuring, and scaling an Azure Kubernetes Service cluster. Also, deploying an Azure Container Registry instance and deploying an application into an Azure Kubernetes Service cluster.

Course Content

Module 1 Plan an Azure Kubernetes Service deployment

In this module, you learn about the core Kubernetes infrastructure components, including control plane nodes, node pools, and workload resources such as pods, deployments, and sets.

Module 2 Deploy and use Azure Container Registry

Learn how to create a private registry service for building, storing, and managing container images and related artifacts.

Module 3 Deploy an Azure Kubernetes Service cluster

In this module, you learn how to create an Azure Kubernetes Service cluster, configure its components, and connect to it using kubectl commands.

Module 4 Configure an Azure Kubernetes Service cluster

Use Azure Policy to enforce policies and safeguards on your Kubernetes clusters at scale. Azure Policy Ensures that your cluster is secure, compliant, and consistent across your organization.

Module 5 Deploy applications to Azure Kubernetes Service

This module covers how to provision an Azure Kubernetes Service cluster and validate the effect of Azure Policy.

Module 6 Configure scaling in Azure Kubernetes Service

This module covers the scaling applications in Azure Kubernetes Service (AKS), including manually scaling pods or nodes and integrating with Azure Container Instances (ACI).

Module 7 Guided Project – Deploy applications to Azure Kubernetes Service

Welcome to this interactive skills validation experience. Completing this module helps prepare you for the Deploy and manage containers with Azure Kubernetes Service assessment.

Course Overview

In this learning path, you practice implementing Azure Monitor to collect, analyze and act on monitoring telemetry from Azure environments. You learn to configure and interpret monitoring for virtual machines, networking, and web applications.

Course Content

Module 1 Create and configure a Log Analytics workspace

Understand how to create and configure a Log Analytics workspace, and how to configure data retention and health status alerts for the workspace.

Module 2 Configure monitoring for applications

Understand how to monitor the performance of your applications and how to collect and analyze the appropriate telemetry to improve application performance.

Module 3 Configure monitoring for virtual machines

Understand how to deploy and configure Azure Monitor Agent on IaaS VMs and how to enable VM Insights and Data Collection Rules to monitor performance and application telemetry.

Module 4 Configure monitoring for virtual networks

Understand how to use Azure Network Watcher Connection Monitor, flow logs, NSG diagnostics, and packet capture to monitor connectivity across your Azure IaaS network resources.

Module 5 Configure alerts and responses

Understand how to configure and manage alerts and responses in order to proactively manage notifications about potential issues before those issues become problems for your users.

Module 6 Guided Project – Deploy and configure Azure Monitor

Understand how to configure monitoring of various workloads and infrastructure services using Azure Monitor.

Course Overview

In this learning path, you prepare for the Applied Skill, Deploy and administer Linux virtual machines on Microsoft Azure.

Course Content

Module 1 Configure virtual machines

Learn how to configure virtual machines including sizing, storage, and connections.

Module 2 Add and size disks in Azure virtual machines

Understand and create the different types of disk storage available to Azure virtual machines (VMs).

Module 3 Monitor your Azure virtual machines with Azure Monitor

Learn how to monitor your Azure VMs by using Azure Monitor to collect and analyze VM host and client metrics and logs.

Module 4 Protect your virtual machines by using Azure Backup

Use Azure Backup to help protect on-premises servers, virtual machines, SQL Server, Azure file shares, and other workloads.

Module 5 Manage virtual machines with the Azure CLI

Learn how to use the cross-platform Azure CLI to create, start, stop, and perform other management tasks related to virtual machines in Azure.

Module 6 Implement access management for Azure resources

Explore how to use built-in Azure roles, managed identities, and RBAC-policy to control access to Azure resources. Identity is the key to secure solutions.

Module 7 Configure Azure Files and Azure File Sync

Learn how to configure Azure Files and Azure File Sync.

Module 8 Copy and move blobs from one container or storage account to another using the AzCopy command

Learn how to use AzCopy to copy and move blobs between contains and storage accounts both synchronously and asynchronously.

Module 9 Guided Project: Deploy and administer Linux virtual machines on Azure

In this guided project module, you prepare and study for the Deploy and administer Linux virtual machines on Azure Applied Skill.

Course Overview

Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift. This course demonstrates how to ingest, store, and transform data in the data warehouse. Topics covered include: the purpose of Amazon Redshift, how Amazon Redshift addresses business and technical challenges, features and capabilities of Amazon Redshift, designing a Data Warehousing Solution on AWS by applying best practices based on the Well-Architected Framework, integration with AWS and non-AWS products and services, performance tuning, orchestration, and securing and monitoring Amazon Redshift.

Course level: Advanced

Duration: 3 days

Activities

This course includes presentations, hands-on labs, and demonstrations.

Course Objectives

In this course, you will learn to:

• Describe Amazon Redshift architecture and its roles in a modern data architecture
• Design and implement a data warehouse in the cloud using Amazon Redshift
• Identify and load data into an Amazon Redshift data warehouse from a variety of sources
• Analyze data using SQL QEV2 notebooks
• Design and implement a disaster recovery strategy for an Amazon Redshift data warehouse
• Perform maintenance and performance tuning on an Amazon Redshift data warehouse
• Secure and manage access to an Amazon Redshift data warehouse
• Share data between multiple Redshift clusters in an organization
• Orchestrate workflows in the data warehouse using AWS Step Functions state machines
• Create an ML model and configure predictors using Amazon Redshift ML

Course Content

Day 1

Module 1: Data Warehouse Concepts

• Modern data architecture
• Introduction to the course story
• Data warehousing with Amazon Redshift
• Amazon Redshift Serverless architecture
• Hands-On Lab: Launch and Configure an Amazon Redshift Serverless Data Warehouse

Module 2: Setting up Amazon Redshift

• Data models for Amazon Redshift
• Data management in Amazon Redshift
• Managing permissions in Amazon Redshift
• Hands-On Lab: Setting up a Data Warehouse using Amazon Redshift Serverless

Module 3: Loading Data

• Overview of data sources
• Loading data from Amazon Simple Storage Service (Amazon S3)
• Extract, transform, and load (ETL) and extract, load, and transform (ELT)
• Loading streaming data
• Loading data from relational databases
• Hands-On Lab: Populating the data warehouse

Day 2

Module 4: Deep Dive into SQL Query Editor v2 and Notebooks

• Features of Amazon Redshift Query Editor v2
• Demonstration: Using Amazon Redshift Query Editor v2
• Advanced queries
• Hands-On Lab: Data Wrangling on AWS

Module 5: Backup and Recovery

• Disaster recovery
• Backing up and restoring Amazon Redshift provisioned
• Backing up and restoring Amazon Redshift Serverless

Module 6: Amazon Redshift Performance Tuning

• Factors that impact query performance
• Table maintenance and materialized views
• Query analysis
• Workload management
• Tuning guidance
• Amazon Redshift monitoring
• Hands-On Lab: Performance Tuning the Data Warehouse

Module 7: Securing Amazon Redshift

• Introduction to Amazon Redshift security and compliance
• Authentication with Amazon Redshift
• Access control with Amazon Redshift
• Data encryption with Amazon Redshift
• Auditing and compliance with Amazon Redshift
• Hands-On Lab: Securing Amazon Redshift

Day 3

Module 8: Orchestration

• Overview of data orchestration
• Orchestration with AWS Step Functions
• Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA)
• Hands-On Lab: Orchestrating the Data Warehouse Pipeline

Module 9: Amazon Redshift ML

• Machine Learning Overview
• Getting started with Amazon Redshift ML
• Amazon Redshift ML workflow scenarios
• Amazon Redshift ML Usage
• Hands-On Lab: Predicting customer churn with Amazon Redshift ML

Module 10: Amazon Redshift Data Sharing

• Overview of data sharing in Amazon Redshift
• Amazon DataZone for Data as a service

Module 11: Wrap-Up

• Hands-On Lab: End of course challenge lab

Course Overview

Learn how to design and build data processing systems.

This four-day instructor-led class provides you with a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, you will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Course Objectives

In this course you will learn:

• Design and build data processing systems on Google Cloud Platform
• Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
• Derive business insights from extremely large
• datasets using Google BigQuery
• Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
• Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
• Enable instant insights from streaming data

Course Content

1. Serverless Data Analysis with BigQuery

• What is BigQuery
• Advanced Capabilities
• Performance and pricing

2. Serverless, Autoscaling Data Pipelines with Dataflow

3. Getting Started with Machine Learning

• What is machine learning (ML)
• Effective ML: concepts, types
• Evaluating ML
• ML datasets: generalization

4. Building ML Models with Tensorflow

• Getting started with TensorFlow
• TensorFlow graphs and loops + lab
• Monitoring ML training

5. Scaling ML Models with CloudML

• Why Cloud ML?
• Packaging up a TensorFlow model
• End-to-end training

6. Feature Engineering

• Creating good features
• Transforming inputs
• Synthetic features
• Preprocessing with Cloud ML

7. ML Architectures

• Wide and deep
• Image analysis
• Embeddings and sequences
• Recommendation systems

8. Google Cloud Dataproc Overview

• Introducing Google Cloud Dataproc
• Creating and managing clusters
• Defining master and worker nodes
• Leveraging custom machine types and preemptible worker nodes
• Creating clusters with the Web Console
• Scripting clusters with the CLI
• Using the Dataproc REST API
• Dataproc pricing
• Scaling and deleting Clusters

9. Running Dataproc Jobs

• Controlling application versions
• Submitting jobs
• Accessing HDFS and GCS
• Hadoop
• Spark and PySpark
• Pig and Hive
• Logging and monitoring jobs
• Accessing onto master and worker nodes with SSH
• Working with PySpark REPL (command-line interpreter)

10. Integrating Dataproc with Google Cloud Platform

• Initialization actions
• Programming Jupyter/Datalab notebooks
• Accessing Google Cloud Storage
• Leveraging relational data with Google Cloud SQL
• Reading and writing streaming Data with Google BigTable
• Querying Data from Google BigQuery
• Making Google API Calls from notebooks

11. Making Sense of Unstructured Data with Google’s Machine Learning APIs

• Google’s Machine Learning APIs
• Common ML Use Cases
• Vision API
• Natural Language API
• Translate
• Speech API

12. Need for Real-Time Streaming Analytics

• What is Streaming Analytics?
• Use-cases
• Batch vs. Streaming (Real-time)
• Related terminologies
• GCP products that help build for high availability, resiliency, high-throughput, real-timestreaming analytics (review of Pub/Sub and Dataflow)

13. Architecture of Streaming Pipelines

• Streaming architectures and considerations
• Choosing the right components
• Windowing
• Streaming aggregation
• Events, triggers

14. Stream Data and Events into PubSub

• Topics and Subscriptions
• Publishing events into Pub/Sub
• Subscribing options: Push vs Pull
• Alerts

15. Build a Stream Processing Pipeline

• Pipelines, PCollections and Transforms
• Windows, Events, and Triggers
• Aggregation statistics
• Streaming analytics with BigQuery
• Low-volume alerts

16. High Throughput and Low-Latency with Bigtable

• Latency considerations
• What is Bigtable
• Designing row keys
• Performance considerations

17. High Throughput and Low-Latency with Bigtable

• What is Google Data Studio?
• From data to decisions

Course Overview

Course Description

Creating and Configuring Production ROSA Clusters (CS220) teaches how to configure ROSA clusters as part of pre-existing AWS environments and how to integrate ROSA with AWS services commonly used by IT operations teams, such as Amazon CloudWatch.

Note: This course is offered as a two day in person class, a three day virtual class or is self-paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, select your location then “get started” on the right hand menu.

Course Content Summary

– Create ROSA STS PrivateLink clusters

– Connect PrivateLink ROSA clusters to existing VPCs and enable administrators and developers to access those clusters

– Configure dedicated machine pools and node/pod autoscaling

– Configure node, cluster, and audit log forwarding to Amazon CloudWatch

– Configure authentication and group sync with Amazon Cognito

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Course Objectives

Impact on the Organization

• Red Hat OpenShift Service on AWS (ROSA) is a turnkey application platform that provides a managed Red Hat OpenShift service that runs natively on Amazon Web Services (AWS) to enable organizations to increase operational efficiency, refocus on innovation, and quickly build, deploy, and scale applications. Red Hat OpenShift is the hybrid cloud platform that brings operational consistency to on-premise and different cloud environments.
• Organizations adopting ROSA are typically existing AWS customers with skills on using AWS services for a variety of business scenarios and need to integrate managed OpenShift clusters with their pre-existing AWS environments. These organizations are usually very security-conscious and require strong access controls and network security for all of their AWS services, including their ROSA clusters.

Impact on the Individual

• After completing CS220, students can create private ROSA clusters which are integrated with AWS infrastructure services typically employed by IT operations teams and ready to start onboarding applications and developers.

Course Content

Private Red Hat OpenShift on AWS (ROSA) Clusters

Create a PrivateLink ROSA cluster with STS and enable developers or administrators to access the API and router endpoints of the cluster.

Node and Pod Autoscaling

Configure a ROSA cluster and a workload to dynamically scale the number of cluster nodes and application pods according to load.

Monitor ROSA Clusters with Amazon CloudWatch

Configure ROSA clusters to forward logs to Amazon CloudWatch for long-term storage, aggregation, and analysis, and to authenticate OpenShift users by using Amazon Cognito.

Course Overview

In this learning path, you’ll see how Azure AI Document Intelligence solutions can enable you to capture data from typed or hand-written forms. You’ll also learn how to build a solution for your custom form types and integrate that solution into an Azure Cognitive Search pipeline. You’ll learn how to:

• Design a solution that analyzes your business forms by using Azure AI Document Intelligence.
• Create a solution that analyzes common documents by using Document Intelligence.
• Create a solution that analyses different custom form types by using Document Intelligence.
• Include an Azure AI Document Intelligence service as a custom skill in an Azure Cognitive Search pipeline.

Course Content

Module 1 Plan an Azure AI Document Intelligence solution

Learn how to use Azure AI Document Intelligence to build solutions that analyze forms and output data for storage or further processing.

• Describe the components of an Azure AI Document Intelligence solution.
• Create and connect to Azure AI Document Intelligence resources in Azure.
• Choose whether to use a prebuilt, custom, or composed model.

Module 2 Use prebuilt Form Recognizer models

Learn what data you can analyze by choosing prebuilt Forms Analyzer models and how to deploy these models in a Form Analyzer solution.

• Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
• Analyze forms by using the General Document, Read, and Layout models.
• Analyze forms by using financial, ID, and tax prebuilt models.

Module 3 Extract data from forms with Azure Document Intelligence

Azure Document Intelligence uses machine learning technology to identify and extract key-value pairs and table data from form documents with accuracy, at scale. This module teaches you how to use the Azure Document Intelligence Azure AI service.

• Identify how Document intelligence’s layout service, prebuilt models, and custom models can automate processes.
• Use Document intelligence’s capabilities with SDKs, REST API, and Document Intelligence Studio.
• Develop and test custom models.

Module 4 Create a composed Form Recognizer model

Learn how to assemble custom models into composed solutions that can analyze different types of your own documents.

• Describe business problems that you would use custom models and composed models to solve.
• Train a custom model to obtain data from forms with unusual structures.
• Create a composed model that can analyze forms in multiple formats.

Module 5 Build a Document Intelligence custom skill for Azure AI Search

Learn how to use an Azure Document Intelligence solution as a custom skill to enrich content in an Azure AI Search pipeline.

• Describe how a custom skill can enrich content passed through an Azure AI Search pipeline.
• Build a custom skill that calls an Azure Forms Analyzer solution to obtain data from forms.

Course Overview

In this learning path, you practice building journeys using Dynamics 365 Customer Insights. The skills validated include creating marketing assets like emails, creating a segment, creating a journey, adding elements to a journey, and publishing the journey.

Course Content

Create emails in Dynamics 365 Customer Insights – Journeys

This module covers emails, assets, and personalization in the real-time work area of Dynamics 365 Customer Insights – Journeys.

• Introduction
• Manage assets
• Create marketing emails
• Personalize content
• Use brand profiles
• Use Copilot to generate email content ideas
• Style emails with AI-assisted themes
• Create content blocks
• Preview and test emails
• Check your message for errors and publish
• Send emails without building a journey
• Edit a live email
• Prevent sending emails to duplicated email addresses
• Check your knowledge
• Summary

Build journeys with Dynamics 365 Customer Insights – Journeys

This module covers segments, triggers, and journeys within the real-time work area of Dynamics 365 Customer Insights – Journeys.

• Introduction
• Create and manage segments
• Work with triggers
• Create a journey
• Add messages to the journey
• Add activities to the journey
• Add other elements to the journey
• Publish journeys
• Check your knowledge
• Summary

Guided project – Create and manage journeys with Dynamics 365 Customer Insights

In this module, practice building journeys in Customer Insights, including creating assets, building a segment, and creating a segment-based or trigger-based journey. This lab offers interactive practice with real-world scenarios for business-specific challenges.

• Introduction
• Prepare for the guided project
• Exercise – Create emails
• Exercise – Create a segment
• Exercise – Create a segment-based journey
• Exercise – Create a trigger-based journey
• Knowledge check
• Summary