Amazon
AMA01 - Advanced Architecting on AWS
Длительность: 3 дня (24 часа)
Код курса: AMA01
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Course description
In this course, each module presents a scenario with an architectural challenge to be solved. You will
examine available AWS services and features as solutions to the problem. You will gain insights by
participating in problem-based discussions and learning about the AWS services that you could apply to
meet the challenges. Over 3 days, the course goes beyond the basics of a cloud infrastructure and covers
topics to meet a variety of needs for AWS customers. Course modules focus on managing multiple AWS
accounts, hybrid connectivity and devices, networking with a focus on AWS Transit Gateway connectivity,
container services, automation tools for continuous integration/continuous delivery (CI/CD), security and
distributed denial of service (DDoS) protection, data lakes and data stores, edge services, migration options,
and managing costs. The course concludes by presenting you with scenarios and challenging you to
identify the best solutions.
Activities
This course includes presentations, group discussions, use cases, videos, assessments, and hands-on labs.
Course objectives
In this course, you will learn to:
Review the AWS Well-Architected Framework to ensure understanding of best cloud design
practices by responding to poll questions while following a graphic presentation
Demonstrate the ability to secure Amazon Simple Storage Service (Amazon S3) virtual private
cloud (VPC) endpoint connections in a lab environment
Identify how to implement centralized permissions management and reduce risk using AWS
Organizations organizational units (OUs) and service control policies (SCPs) with AWS Single SignOn
Compare the permissions management capabilities of OUs, SCPs, and AWS SSO with and without
AWS Control Tower to determine best practices based on use cases
Discuss AWS hybrid network designs to address traffic increases and streamline remote work while ensuring FIPS 140-2 Level 2, or Level 3 security compliance
Explore the solutions and products available to design a hybrid infrastructure, including access to
5G networks, to optimize service and reduce latency while maintaining high security for critical onpremises applications
Explore ways to simplify the connection configurations between applications and highperformance workloads across global networks
Demonstrate the ability to configure a transit gateway in a lab environment
Identify and discuss container solutions and define container management options
Build and test a container in a lab environment
Examine how the AWS developer tools optimize the CI/CD pipeline with updates based on nearreal-time data
Identify the anomaly detection and protection services that AWS offers to defend against DDoS attacks
Identify ways to secure data in transit, at rest, and in use with AWS Key Management Service (AWS KMS) and AWS Secrets Manager
Determine the best data management solution based on frequency of access, and data query and analysis needs
Set up a data lake and examine the advantages of this type of storage configuration to crawl and query data in a lab environment
Identify solutions to optimize edge services to eliminate latency, reduce inefficiencies, and mitigate risks
Identify the components used to automate the scaling of global applications using geolocation and traffic control
Deploy and activate an AWS Storage Gateway file gateway and AWS DataSync in a lab environment
Review AWS cost management tools to optimize costs while ensuring speed and performance
Review migration tools, services, and processes that AWS provides to implement effective cloud operation models based on use cases and business needs
Provide evidence of your ability to apply the technical knowledge and experience gained in the course to improve business practices by completing a Capstone Project
Course outline
Day 1
Module 1: Reviewing Architecting Concepts
Group Exercise: Review Architecting on AWS core best practices
Lab 1: Securing Amazon S3 VPC Endpoint Communications
Module 2: Single to Multiple Accounts
AWS Organizations for multi-account access and permissions
AWS SSO to simplify access and authentication across AWS accounts and third-party services
AWS Control Tower
Permissions, access, and authentication
Module 3: Hybrid Connectivity
AWS Client VPN authentication and control
AWS Site-to-Site VPN
AWS Direct Connect for hybrid public and private connections
Increasing bandwidth and reducing cost
Basic, high, and maximum resiliency
Amazon Route 53 Resolver DNS resolution
Module 4: Specialized Infrastructure
AWS Storage Gateway solutions
On-demand VMware Cloud on AWS
Extending cloud infrastructure services with AWS Outposts
AWS Local Zones for latency-sensitive workloads
Your 5G network with and without AWS Wavelength
Module 5: Connecting Networks
Simplifying private subnet connections
VPC isolation with a shared services VPC
Transit Gateway Network Manager and VPC Reachability Analyzer
AWS Resource Access Manager
AWS PrivateLink and endpoint services
Lab 2: Configuring Transit Gateways
Day 2
Module 6: Containers
Container solutions compared to virtual machines
Docker benefits, components, solutions architecture, and versioning
Container hosting on AWS to reduce cost
Managed container services: Amazon Elastic Container Service (Amazon ECS) and Amazon
Elastic Kubernetes Service (Amazon EKS)
AWS Fargate
Lab 3: Deploying an Application with Amazon ECS on Fargate
Module 7: Continuous Integration/Continuous Delivery (CI/CD)
CI/CD solutions and impact
CI/CD automation with AWS CodePipeline
Deployment models
AWS CloudFormation StackSets to improve deployment management
Module 8: High Availability and DDoS Protection
Common DDoS attacks layers
AWS WAF
AWS WAF web access control lists (ACLs), real-time metrics, logs, and security automation
AWS Shield Advanced services and AWS DDoS Response Team (DRT) services
AWS Network Firewall and AWS Firewall Manager to protect accounts at scale
Module 9: Securing Data
What cryptography is, why you would use it, and how to use it
AWS KMS
AWS CloudHSM architecture
FIPS 140-2 Level 2 and Level 3 encryption
Secrets Manager
Module 10: Large-Scale Data Stores
Amazon S3 data storage management including storage class, inventory, metrics, and policies
Data lake vs. data warehouse: Differences, benefits, and examples
AWS Lake Formation solutions, security, and control
Lab 4: Setting Up a Data Lake with Lake Formation
Day 3
Module 11: Large-Scale Applications
What edge services are and why you would use them
Improve performance and mitigate risk with Amazon CloudFront
Lambda@Edge
AWS Global Accelerator: IP addresses, intelligent traffic distribution, and health checks
Lab 5: Migrating an On-Premises NFS Share Using AWS DataSync and Storage Gateway
Module 12: Optimizing Cost
On-premises and cloud acquisition/deprecation cycles
Cloud cost management tools including reporting, control, and tagging
Examples and analysis of the five pillars of cost optimization
Module 13: Migrating Workloads
Business drivers and the process for migration
Successful customer practices
The 7 Rs to migrate and modernize
Migration tools and services from AWS
Migrating databases and large data stores
AWS Schema Conversion Tool (AWS SCT)
Module 14: Capstone Project
Use the Online Course Supplement (OCS) to review use cases, investigate data, and answer architecting design questions about Transit Gateway, hybrid connectivity, migration, and cost optimization
AMA02 - Advanced AWS Well-Architected Best Practices
Длительность: 1 день (8 часов)
Код курса: АМА02
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Course description
This hands-on, advanced technical-level, instructor-led course provides a deep dive into Amazon Web
Services (AWS) best practices to help you perform effective and efficient AWS Well-Architected Framework
reviews. The course covers the phases of a review, including how to prepare, run, and get guidance after a
review has been performed. This course is designed for AWS customers and AWS Partners. Attendees
should have familiarity with the AWS concepts, terminology, services, and tools that are covered in the
intermediate 200-level precursor to this course. This course provides an AWS Well-Architected Framework
review simulation and instructor-led group exercises and discussions about prioritizing and resolving risks.
The content focuses on how to prepare proposals on high and medium risk issues using the AWS WellArchitected Tool.
Activities
This course includes presentations, demonstrations, group exercise sessions, and knowledge checks.
Course objectives
In this course, you will learn to:
Recognize workload definition and key concepts
Identify the AWS Well-Architected Framework review phases, process, best practices, and antipatterns
Identify high and medium risks
Prioritize improvements to the AWS Well-Architected workflow
Locate and use the AWS Well-Architected Framework white paper, labs, and prebuilt solutions in
the AWS solutions library
Locate and use AWS Well-Architected independent software vendors (ISVs)
Locate and use the AWS Well-Architected Partner Program (WAPP)
Intended audience
This course is intended for:
Solutions architects
Cloud practitioners
Data engineers
Data scientists
Developers
Prerequisites
We recommend that attendees of this course have:
Completed AWS Well-Architected Best Practices Intermediate – L200
Course outline
Module 0: Course Introduction
Module 1: AWS Well-Architected Framework Reviews
AWS Well-Architected Framework workload
AWS Well-Architected Framework review phases
AWS Well-Architected review approach, lessons learned, and use cases
AWS Well-Architected review best practices
AWS Well-Architected review anti-patterns
Knowledge check
Module 2: Customer Scenario Group Sessions
Customer Story
Demonstration of the workflow
Hands-on group exercise
Demonstration: Running a review in the Operational Excellence pillar
Role-play exercise: Running a review in the Security pillar
Role-play exercise: Running a review in the Reliability pillar
Role-play exercise: Running a review in the Performance Efficiency pillar
Role-play exercise: Running a review in the Cost Optimization pillar
Module 3: Risk Solutions and Priorities
AWS Well-Architected Framework review engagement workflow
High risk and medium risk issues
Defining risks
Resolving high-risk issues (HRIs) and medium-risk issues (MRIs)
Group discussion: Identifying and resolving significant risks for:
Operational Excellence
Security
Reliability
Performance Efficiency
Cost Optimization
Prioritizing improvements
AWS Well-Architected improvement workflow
Module 4: Resources
Resource pages
AWS Well-Architected ISVs
AWS Well-Architected Partner Program (WAPP)
Module 5: Course Summary
Debrief
What’s next?
Course feedback
AMA03 - Advanced Developing on AWS
Длительность: 3 дня (24 часа)
Код курса: AMA03
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Course description
The Advanced Developing on AWS course uses the real-world scenario of taking a legacy, on-premises
monolithic application and refactoring it into a serverless microservices architecture. This three-day
advanced course covers advanced development topics such as architecting for a cloud-native environment;
deconstructing on-premises, legacy applications and repackaging them into cloud-based, cloud native
architectures; and applying the tenets of the Twelve-Factor Application methodology.
Activities
This course includes presentations, group exercises, and hands-on labs.
Course objectives
In this course, you will:
Analyze a monolithic application architecture to determine logical or programmatic break points where the application can be broken up across different AWS services
Apply Twelve-Factor Application manifesto concepts and steps while migrating from a monolithic architecture
Recommend the appropriate AWS services to develop a microservices based cloud-native application
Use the AWS API, CLI, and SDKs to monitor and manage AWS services
Migrate a monolithic application to a microservices application using the 6 Rs of migration
Explain the SysOps and DevOps interdependencies necessary to deploy a microservices application in AWS
Intended audience
This course is intended for experienced software developers who are already familiar with AWS services.
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
Completion of the Developing on AWS classroom training, and then a minimum of 6 months of
application of those concepts in a real world environment
Enroll today
Visit aws.training to find a class today
Course outline
Day 1
Module 1: The cloud journey
Common off-cloud architecture
Introduction to Cloud Air
Monolithic architecture
Migration to the cloud
Guardrails
The six R’s of migration
The Twelve-Factor Application Methodology
Architectural styles and patterns
Overview of AWS Services
Interfacing with AWS Services
Authentication
Infrastructure as code and Elastic Beanstalk
Demonstration: Walk through creating base infrastructure with AWS CloudFormation in the
AWS console
Hands-on lab 1: Deploy your monolith application using AWS Elastic Beanstalk
Module 2: Gaining Agility
DevOps
CI/CD
Application configuration
Secrets management
CI/CD Services in AWS
Demonstration: Demo AWS Secrets Manager
Day 2
Module 5: Monolith to MicroServices
Microservices
Serverless
A look at Cloud Air
Microservices using Lambda and API Gateway
SAM
Strangling the Monolith
Hands-on lab: Using AWS Lambda to develop microservices
Module 6: Polyglot Persistence & Distributed Complexity
Polyglot persistence
DynamoDB best practices
Distributed complexity
Step functions
Day 3
Module 5: Resilience and Scale
Decentralized data stores
Amazon SQS
Amazon SNS
Amazon Kinesis Streams
AWS IoT Message Broker
Serverless event bus
Event sourcing and CQRS
Designing for resilience in the cloud
Hands-on lab: Exploring the AWS messaging options
Module 6: Security and Observability
Serverless Compute with AWS Lambda
Authentication with Amazon Cognito
Debugging and traceability
Hands-on lab: Developing microservices on AWS
Hands-on lab 8: Automating deployments with Cloud Formation
AMA04 - Amazon SageMaker Studio for Data Scientists
Длительность: 3 дня (24 часа)
Код курса: АМА04
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Course description
Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine
learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for
ML. This course prepares experienced data scientists to use the tools that are a part of SageMaker
Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve
productivity at every step of the ML lifecycle.
Activities
This course includes presentations, hands-on labs, demonstrations, discussions, and a capstone project.
Course objectives
In this course, you will learn to:
• Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using
Amazon SageMaker Studio
Intended audience
This course is intended for:
Experienced data scientists who are proficient in ML and deep learning fundamentals
Prerequisites
We recommend that all attendees of this course have:
Experience using ML frameworks
Python programming experience
At least 1 year of experience as a data scientist responsible for training, tuning, and
deploying models
AWS Technical Essentials digital or classroom training
Enroll today
Visit aws.training to find a class today
Course outline
Day 1
Module 1: Amazon SageMaker Studio Setup
JupyterLab Extensions in SageMaker Studio
Demonstration: SageMaker user interface demo
Module 2: Data Processing
Using SageMaker Data Wrangler for data processing
Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
Using Amazon EMR
Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
Using AWS Glue interactive sessions
Using SageMaker Processing with custom scripts
Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
SageMaker Feature Store
Hands-On Lab: Feature engineering using SageMaker Feature Store
Module 3: Model Development
SageMaker training jobs
Built-in algorithms
Bring your own script
Bring your own container
SageMaker Experiments
Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
Day 2
Module 3: Model Development (continued)
SageMaker Debugger
Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
Automatic model tuning
SageMaker Autopilot: Automated ML
Demonstration: SageMaker Autopilot
Bias detection
Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
SageMaker Jumpstart
Module 4: Deployment and Inference
SageMaker Model Registry
SageMaker Pipelines
Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
SageMaker model inference options Scaling
Testing strategies, performance, and optimization
Hands-On Lab: Inferencing with SageMaker Studio
Module 5: Monitoring
Amazon SageMaker Model Monitor
Discussion: Case study
Demonstration: Model Monitoring
Day 3
Module 6: Managing SageMaker Studio Resources and Updates
Accrued cost and shutting down
Updates Capstone
Environment setup
Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
Challenge 2: Create feature groups in SageMaker Feature Store
Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
(Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization
Challenge 5: Evaluate the model for bias using SageMaker Clarify
Challenge 6: Perform batch predictions using model endpoint
(Optional) Challenge 7: Automate full model development process using SageMaker Pipeline
AMA05 - Architecting on AWS
Длительность: 3 дня (24 часа)
Код курса: AMA05
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Course description
Architecting on AWS is for solutions architects, solution-design engineers, and developers seeking an
understanding of AWS architecting. In this course, you will learn to identify services and features to build
resilient, secure, and highly available IT solutions on the AWS Cloud.
Architectural solutions differ depending on industry, types of applications, and business size. AWS
Authorized Instructors emphasize best practices using the AWS Well-Architected Framework, and guide
you through the process of designing optimal IT solutions based on real-life scenarios. The modules focus
on account security, networking, compute, storage, databases, monitoring, automation, containers,
serverless architecture, edge services, and backup and recovery. At the end of the course, you will practice
building a solution and apply what you have learned.
Activities
This course includes presentations based on use cases. It also includes group discussions, demonstrations,
assessments, and hands-on labs.
Course objectives
In this course, you will learn to:
Identify AWS architecting basic practices
Summarize the fundamentals of account security
Identify strategies to build a secure virtual network that includes private and public subnets
Practice building a multi-tier architecture in AWS
Identify strategies to select the appropriate compute resources based on business use cases
Compare and contrast AWS storage products and services based on business scenarios
Compare and contrast AWS database services based on business needs
Identify the role of monitoring, load balancing, and auto scaling responses based on business needs
Identify and discuss AWS automation tools that will help you build, maintain, and evolve your infrastructure
Discuss hybrid networking, network peering, and gateway and routing solutions to extend and secure your infrastructure
Explore AWS container services for the rapid implementation of an infrastructure-agnostic, portable application environment
Identify the business and security benefits of AWS serverless services based on business examples
Discuss the ways in which AWS edge services address latency and security
Explore AWS backup, recovery solutions, and best practices to ensure resiliency and business continuity
Intended audience
This course is intended for:
Solution architects
Solution-design engineers
Developers seeking an understanding of AWS architecting
Individuals seeking the AWS Solutions Architect-Associate certification
Prerequisites
We recommend that attendees of this course have:
Completed AWS Cloud Practitioner Essentials, or AWS Technical Essentials
Working knowledge of distributed systems
Familiarity with general networking concepts
Familiarity with IP addressing
Working knowledge of multi-tier architectures
Familiarity with cloud computing concepts
Enroll today
Visit aws.training to find a class today
Course outline
Day 1
Module 1: Architecting Fundamentals
AWS services
AWS infrastructure
AWS Well-Architected Framework
Hands-on lab: Explore and interact with the AWS Management Console and AWS Command
Line Interface
Module 2: Account Security
Principals and identities
Security policies
Managing multiple accounts
Module 3: Networking 1
IP addressing
VPC fundamentals
VPC traffic security
Module 4: Compute
Compute services
EC2 instances
Storage for EC2 instances
Amazon EC2 pricing options
AWS Lambda
Hands-On Lab: Build your Amazon VPC infrastructure
Day 2
Module 5: Storage
Storage services
Amazon S3
Shared file systems
Data migration tools
Module 6: Database Services
Database services
Amazon RDS
Amazon DynamoDB
Database caching
Database migration tools
Hands-on Lab: Create a database layer in your Amazon VPC infrastructure
Module 7: Monitoring and Scaling
Monitoring
Alarms and events
Load balancing
Auto scaling
Hands-on Lab: Configure high availability in your Amazon VPC
Module 8: Automation
AWS CloudFormation
Infrastructure management
Module 9: Containers
Microservices
Containers
Container services
Day 3
Module 10: Networking 2
VPC endpoints
VPC peering
Hybrid networking
AWS Transit Gateway
Module 11: Serverless
What is serverless?
Amazon API Gateway
Amazon SQS
Amazon SNS
Amazon Kinesis
AWS Step Functions
Hands-on Lab: Build a serverless architecture
Module 12: Edge Services
Edge fundamentals
Amazon Route 53
Amazon CloudFront
DDoS protection
AWS Outposts
Hands-On Lab: Configure an Amazon CloudFront distribution with an Amazon S3 origin
Module 13: Backup and Recovery
Disaster planning
AWS Backup
Recovery strategies
Hands-on Lab: Capstone lab – Build an AWS Multi-Tier architecture. Participants review the
concepts and services learned in class and build a solution based on a scenario. The lab
environment provides partial solutions to promote analysis and reflection. Participants deploy
a highly available architecture. The instructor is available for consultation.
AMA06 - Authoring Visual Analytics Using Amazon QuickSight
Длительность: 2 дня (16 часов)
Код курса: АМА06
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Course description
In this course, you will build a data visualization solution using Amazon QuickSight. QuickSight allows
everyone in your organization to understand your data by exploring through interactive dashboards, asking
questions in natural language, or automatically looking for patterns and outliers powered by machine
learning. This course focuses on connecting to data sources, building visuals, designing interactivity, and
creating calculations. You will learn how to apply security best practices to your analyses. You will also
explore the machine learning capabilities built into QuickSight.
Activities
This course includes presentations, demonstrations, group exercises, and practice and challenge labs.
Course objectives
In this course, you will learn to:
Explain the benefits, use cases, and key features of Amazon QuickSight
Design, create, and customize QuickSight dashboards to visualize data and extract business insights from it
Select and configure appropriate visualization types to identify, explore, and drill down on business insights
Describe how to use one-click embed to incorporate analytics into applications
Connect, transform, and prepare data for dashboarding consumption
Perform advanced data calculations on QuickSight analyses
Describe the security mechanisms available for Amazon QuickSight
Apply fine-grained access control to a dataset
Implement machine learning on data sets for anomaly detection and forecasting
Explain the benefits and key features of QuickSight Q to enhance the dashboard user experience
Intended audience
This course is intended for:
Data and business analysts who build and manage business analytics dashboards
Prerequisites
Students with a minimum one-year experience authoring visual analytics will benefit from this course. We recommend that attendees of this course have:
AWS Classroom Training
© 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Completed Data Analytics Fundamentals
Enroll today
Visit aws.training to find a class today
Course outline
Module 1: Introduction and Overview of Amazon QuickSight
Introducing Amazon QuickSight
Why use Amazon QuickSight for data visualization
Module 2: Getting Started with Amazon QuickSight
Interacting with Amazon QuickSight
Loading data into Amazon QuickSight
Visualizing data in Amazon QuickSight
Demonstration: Walkthrough of Amazon QuickSight interface
Practice Lab: Create your first dashboard
Module 3: Enhancing and Adding Interactivity to Your Dashboard
Enhancing your dashboard
Demonstration: Optimize the size, layout, and aesthetics of a dashboard
Enhancing visualizations with interactivity
Demonstration: Walkthrough of dashboard interactivity features
Practice Lab: Enhancing your dashboard
Module 4: Preparing Datasets for Analysis
Working with datasets
Demonstration: Transform your datasets for analysis
Practice Lab: Preparing data for analysis
Module 5: Performing Advanced Data Calculations
Transform data using advanced calculations
Practice Lab: Performing advanced data calculations
Activity: Designing a Visual Analytics Solution
Day 2
Module 6: Overview of Amazon QuickSight Security and Access Control
Overview of Amazon QuickSight security and access control
Dataset access control in Amazon QuickSight
Lab: Implementing access control in Amazon QuickSight visualizations
Module 7: Exploring machine learning capabilities
Introducing Machine Learning (ML) insights
Natural Language Query with QuickSight Q
Demonstration: Using QuickSight Q
Lab: Using machine learning for anomaly detection and forecasting
End of day challenge labs
Join data sources together
Create a dashboard
Enhance the dashboard and add interactivity
Perform advanced data calculations
Integrate machine learning tools into the dashboard
AMA07 - AWS Cloud Essentials for Business Leaders
Длительность: 4 дня (32 часа)
Код курса: АМА07
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Course description
In this course, you will learn the fundamental concepts of cloud computing and how a cloud strategy can help companies meet business objectives. It explores the advantages and possibilities of cloud computing. It also introduces addresses concepts such as security and compliance to help facilitate better discussions with line of business (LOB) professionals and executives.
Activities
This course includes presentations, case studies, role play, group exercises, and knowledge checks.
Course objectives
In this course, you will learn to:
Explain the role of information technology (IT) in an organization for business transformation
Explain the customer value proposition for using the cloud across industries
Define key characteristics of cloud computing
Explain the cloud business model
Identify key security practices of cloud computing
Frame the cloud business value using the Cloud Value Framework
Intended audience
This course is intended for:
Line of Business (LoB) owners and executives
Prerequisites
We recommend that attendees of this course have:
No prior IT experience or cloud experience is required.
Course outline
Module 1: Course Introduction
Module 2: Information Technology for Business Transformation
Role of IT in an organization for business transformation
Brief history of IT
Legacy approach to IT
What drives customers to move from traditional infrastructure to the cloud
Module 3: Cloud Computing
Define cloud computing
Key characteristics of cloud technology
The cloud business model
Key security practices within the cloud
Module 4: Business Value of the Cloud
The customer value proposition
Identify who is using cloud computing
Industry trends
Customer examples
Module 5: The Cloud Value Framework
Introduction to the Cloud Value Framework
Cost Savings
Staff Productivity
Operational Resilience
Business Agility
Module 6: Business Value Activity
Using a fictional customer case study, we review and apply lessons learned from the course
AMA08 - AWS Cloud Essentials for Business Leaders - Financial Services
Длительность: 4 дня (32 часа)
Код курса: АМА08
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Course description
In this course, you will learn the fundamental concepts of cloud computing and how a cloud strategy can help companies in the financial services industries (FSI) meet business objectives. It explores the advantages and possibilities of cloud computing in banking, insurance, capital
markets, payments, and financial technology. The course addresses concepts such as security, fraud detection, analytics, and compliance to help facilitate discussions with line of business (LOB) professionals and executives
Activities
This course includes presentations, case studies, role play, group exercises, and knowledge checks.
Course objectives
In this course, you will learn to:
Explain the role of information technology (IT) in an organization for business transformation
Explain the customer value proposition for using the cloud in the financial services industry (FSI)
Define key characteristics of cloud computing
Explain the cloud business model
Identify key Financial Services Industry (FSI) security practices of cloud computing
Frame the cloud business value using the Cloud Value Framework
Intended audience
This course is intended for:
Line of business (LOB) owners and executives
Prerequisites
We recommend that attendees of this course have:
No prior IT experience or cloud experience is required.
Course outline
Module 1: Course Introduction
Module 2: Information Technology for Business Transformation
Role of IT in an organization for business transformation
Brief history of IT
Legacy approach to IT
What drives customers to move from traditional infrastructure to the cloud
Module 3: Cloud Computing
Define cloud computing
Key characteristics of cloud technology
The cloud business model
Key FSI security practices within the cloud
Module 4: Business Value of the Cloud
The customer value proposition
Identify who is using cloud computing
Industry trends
Customer examples
Module 5: The Cloud Value Framework
Introduction to the Cloud Value Framework
Cost Savings
Staff Productivity
Operational Resilience
Business Agility
Module 6: Business Value Activity
Using a fictional customer case study, we review and apply lessons learned from the course
AMA09 - AWS Cloud Practitioner Essentials
Длительность: 1 день (8 часов)
Код курса: АМА09
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Course description
This course is for individuals who seek an overall understanding of the Amazon Web Services (AWS) Cloud,
independent of specific technical roles. You will learn about AWS Cloud concepts, AWS services, security,
architecture, pricing, and support to build your AWS Cloud knowledge. This course also helps you prepare
for the AWS Certified Cloud Practitioner exam.
Activities
This course includes presentations, class activities, and knowledge checks.
Course objectives
In this course, you will learn to:
Summarize the working definition of AWS
Differentiate between on-premises, hybrid-cloud, and all-in cloud
Describe the basic global infrastructure of the AWS Cloud
Explain the six benefits of the AWS Cloud
Describe and provide an example of the core AWS services, including compute, network, databases, and storage
Identify an appropriate solution using AWS Cloud services with various use cases
Describe the AWS Well-Architected Framework
Explain the shared responsibility model
Describe the core security services within the AWS Cloud
Describe the basics of AWS Cloud migration
Articulate the financial benefits of the AWS Cloud for an organization’s cost management
Define the core billing, account management, and pricing models
Explain how to use pricing tools to make cost-effective choices for AWS services
Intended audience
This course is intended for:
Sales
Legal
Marketing
Business analysts
Project managers
AWS Academy students
Other IT-related professionals
Prerequisites
We recommend that attendees of this course have:
General IT business knowledge
General IT technical knowledge
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Course outline
Module 1: Introduction to Amazon Web Services
Summarize the benefits of AWS
Describe differences between on-demand delivery and cloud deployments
Summarize the pay-as-you-go pricing model
Module 2: Compute in the Cloud
Describe the benefits of Amazon Elastic Compute Cloud (Amazon EC2) at a basic level
Identify the different Amazon EC2 instance types
Differentiate between the various billing options for Amazon EC2
Describe the benefits of Amazon EC2 Auto Scaling
Summarize the benefits of Elastic Load Balancing
Give an example of the uses for Elastic Load Balancing
Summarize the differences between Amazon Simple Notification Service (Amazon SNS) and Amazon Simple Queue Services (Amazon SQS)
Summarize additional AWS compute options
Module 3: Global Infrastructure and Reliability
Summarize the benefits of the AWS Global Infrastructure
Describe the basic concept of Availability Zones
Describe the benefits of Amazon CloudFront and Edge locations
Compare different methods for provisioning AWS services
Module 4: Networking
Describe the basic concepts of networking
Describe the difference between public and private networking resources
Explain a virtual private gateway using a real life scenario
Explain a virtual private network (VPN) using a real life scenario
Describe the benefit of AWS Direct Connect
Describe the benefit of hybrid deployments
Describe the layers of security used in an IT strategy
Describe which services are used to interact with the AWS global network
Module 5: Storage and Databases
Summarize the basic concept of storage and databases
Describe benefits of Amazon Elastic Block Store (Amazon EBS)
Describe benefits of Amazon Simple Storage Service (Amazon S3)
Describe the benefits of Amazon Elastic File System (Amazon EFS)
Summarize various storage solutions
Describe the benefits of Amazon Relational Database Service (Amazon RDS)
Describe the benefits of Amazon DynamoDB
Summarize various database services
Module 6: Security
Explain the benefits of the shared responsibility model
Describe multi-factor authentication (MFA)
Differentiate between the AWS Identity and Access Management (IAM) security levels
Describe security policies at a basic level
Explain the benefits of AWS Organizations
Summarize the benefits of compliance with AWS
Explain primary AWS security services at a basic level
Module 7: Monitoring and Analytics
Summarize approaches to monitoring your AWS environment
Describe the benefits of Amazon CloudWatch
Describe the benefits of AWS CloudTrail
Describe the benefits of AWS Trusted Advisor
Module 8: Pricing and Support
Understand AWS pricing and support models
Describe the AWS Free Tier
Describe key benefits of AWS Organizations and consolidated billing
Explain the benefits of AWS Budgets
Explain the benefits of AWS Cost Explorer
Explain the primary benefits of the AWS Pricing Calculator
Distinguish between the various AWS Support Plans
Describe the benefits of AWS Marketplace
Module 9: Migration and Innovation
Understand migration and innovation in the AWS Cloud
Summarize the AWS Cloud Adoption Framework (AWS CAF)
Summarize six key factors of a cloud migration strategy
Describe the benefits of various AWS data migration solutions, such as AWS Snowcone, AWS
Snowball, and AWS Snowmobile
Summarize the broad scope of innovative solutions that AWS offers
Summarize the five pillars of the AWS Well-Architected Framework
Module 10: AWS Certified Cloud Practitioner Basics
Determine resources for preparing for the AWS Certified Cloud Practitioner examination
Describe benefits of becoming AWS Certified
AMA10 - AWS Migration Essentials
Длительность: 1 день (8 часов)
Код курса: АМА10
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Course description
This course is intended to provide solutions architects with the foundational knowledge required to
successfully plan and perform lift and shift migrations to the AWS Cloud. In this course you will learn about
methodologies for discovering, planning, performing, and tracking migrations by using various AWS tools and services.
Activities
This course includes presentations, hands-on labs, demonstrations, and assessments.
Course objectives
In this course, you will learn to:
Determine cloud readiness and migration strategies using assessment tools and services
provided by Amazon Web Services (AWS).
Articulate the key tasks involved in planning and mobilizing migrations.
Describe, at a high level, the Amazon Web Services (AWS) services, resources, and tools
necessary for migrations of data and databases.
Describe, at a high level, the Amazon Web Services (AWS) services, resources, and tools
necessary for migrations of applications.
Intended audience
This course is intended for:
Technical audience (solutions architects, developers, and administrators) with limited to no knowledge on cloud migration.
Prerequisites
We recommend that attendees of this course have:
AWS Technical Essentials
Course outline
Day 1
Module 0: Course Introduction
Introductions
Course overview
Module 1: Assess
Migration phases
Migration drivers and outcomes
Cloud Adoption Readiness Tool (CART)
Migration Readiness Assessment (MRA)
Migration Evaluator
Migration Portfolio Assessment (MPA)
Module 2: Mobilize
Landing zones
AWS Application Discovery Service
Migration strategies
AWS Migration Hub
Module 3: Migrate and Modernize: Database and Data Migration
AWS Database Migration Service (AWS DMS)
Data migration
Lab 1: Database Migration with AWS DMS
Module 4: Migrate and Modernize: AWS Application Migration Service
Migrate servers with AWS Application Migration Service (AWS MGN)
Modernization phases
AWS Well-Architected Framework for migration
Application optimization
Lab 2: Application Migration with AWS MGN
Module 5: Course Summary
Course summary
AMA11 - AWS Security Best Practices
Course description
Currently, the average cost of a security breach can be upwards of $4 million. AWS Security Best Practices
provides an overview of some of the industry best practices for using AWS security and control types. This
course helps you understand your responsibilities while providing valuable guidelines for how to keep your
workload safe and secure. You will learn how to secure your network infrastructure using sound design
options. You will also learn how you can harden your compute resources and manage them securely.
Finally, by understanding AWS monitoring and alerting, you can detect and alert on suspicious events to
help you quickly begin the response process in the event of a potential compromise.
Activities
This course includes presentations, demonstrations, and hands-on labs.
Course objectives
In this course, you will learn to:
Design and implement a secure network infrastructure
Design and implement compute security
Design and implement a logging solution
Intended audience
This course is intended for:
Solutions architects, cloud engineers, including security engineers, delivery and implementation
engineers, professional services, and Cloud Center of Excellence (CCOE)
Prerequisites
Before attending this course, participants should have completed the following:
AWS Security Fundamentals
AWS Security Essentials
Course outline
Module 1: AWS Security Overview
Shared responsibility model
Customer challenges
Frameworks and standards
Establishing best practices
Compliance in AWS
Module 2: Securing the Network
Flexible and secure
Security inside the Amazon Virtual Private Cloud (Amazon VPC)
Security services
Third-party security solutions
Lab 1: Controlling the Network
Create a three-security zone network infrastructure.
Implement network segmentation using security groups, Network Access Control Lists (NACLs),
and public and private subnets.
Monitor network traffic to Amazon Elastic Compute Cloud (EC2) instances using VPC flow logs.
Module 3: Amazon EC2 Security
Compute hardening
Amazon Elastic Block Store (EBS) encryption
Secure management and maintenance
Detecting vulnerabilities
Using AWS Marketplace
Lab 2: Securing the starting point (EC2)
Create a custom Amazon Machine Image (AMI).
Deploy a new EC2 instance from a custom AMI.
Patch an EC2 instance using AWS Systems Manager.
Encrypt an EBS volume.
Understand how EBS encryption works and how it impacts other operations.
Use security groups to limit traffic between EC2 instances to only that which is encrypted.
Module 4: Monitoring and Alerting
Logging network traffic
Logging user and Application Programming Interface (API) traffic
Visibility with Amazon CloudWatch
Enhancing monitoring and alerting
Verifying your AWS environment
Lab 3: Security Monitoring
Configure an Amazon Linux 2 instance to send log files to Amazon CloudWatch.
Create Amazon CloudWatch alarms and notifications to monitor for failed login attempts.
Create Amazon CloudWatch alarms to monitor network traffic through a Network Address
Translation (NAT) gateway.
AMA12 - AWS Security Essentials
Длительность: 1 день (8 часов)
Код курса: АМА12
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Course description
This course covers fundamental Amazon Web Services (AWS) security concepts, including AWS access control, data encryption methods, and how to secure network access to your AWS infrastructure. Based on the AWS Shared Responsibility Model, you learn your responsibilities related to implementing security in the AWS Cloud and which security-oriented services are available to you. You also learn why and how the
security services help meet the security needs of your organization.
Activities
This course includes presentations and hands-on labs.
Course objectives
In this course, you will learn to:
Identify security benefits and responsibilities of using the AWS Cloud.
Describe the access control and management features of AWS.
Explain the available methods for encrypting data at rest and in transit.
Describe how to secure network access to your AWS resources.
Determine which AWS services can be used for monitoring and incident response.
Intended audience
This course is intended for:
Security IT business-level professionals interested in cloud security practices
Security professionals with minimal to no working knowledge of AWS
Prerequisites
We recommend that attendees of this course have:
Working knowledge of IT security practices and infrastructure concepts and familiarity with cloud
computing concepts.
Course outline
Course Introduction
Module 1: Exploring the Security Pillar
AWS Well-Architected Framework: Security Pillar
Module 2: Security of the Cloud
Shared responsibility model
AWS Global Infrastructure
Compliance and governance
Module 3: Identity and Access Management
Identity and access management
Data access and protection essentials
Lab 1: Introduction to Security Policies
Module 4: Protecting Infrastructure and Data
Protecting your network infrastructure
Edge Security
DDoS Mitigation
Protecting compute resources
Lab 2: Securing VPC Resources with Security Groups
Module 5: Detection and Response
Monitoring and detective controls
Incident response essentials
Module 6: Course Wrap-Up
Course review
AMA13 - AWS Security Governance at Scale
Длительность: 1 день (8 часов)
Код курса: АМА13
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Course description
Security is foundational to AWS. Governance at scale is a new concept for automating cloud governance
that can help companies retire manual processes in account management, budget enforcement, and
security and compliance. By automating common challenges, companies can scale without inhibiting
agility, speed, or innovation. In addition, they can provide decision makers with the visibility, control, and
governance necessary to protect sensitive data and systems.
In this course, you will learn how to facilitate developer speed and agility, and incorporate preventive and
detective controls. By the end of this course, you will be able to apply governance best practices.
Activities
This course includes presentations, demonstrations, and assessments.
Course objectives
In this course, you will learn to:
Establish a landing zone with AWS Control Tower
Configure AWS Organizations to create a multi-account environment
Implement identity management using AWS Single Sign-On users and groups
Federate access using AWS SSO
Enforce policies using prepackaged guardrails
Centralize logging using AWS CloudTrail and AWS Config
Enable cross-account security audits using AWS Identity and Access Management (IAM)
Define workflows for provisioning accounts using AWS Service Catalog and AWS Security Hub
Intended audience
This course is intended for:
Solutions architects, security DevOps, and security engineers
Prerequisites
Before attending this course, participants should have completed the following:
Required:
AWS Security Fundamentals course
AWS Security Essentials course
Optional:
AWS Cloud Management Assessment
Introduction to AWS Control Tower course
Automated Landing Zone course
Introduction to AWS Service Catalog course
Enroll today
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Course outline
Course Introduction
Instructor introduction
Learning objectives
Course structure and objectives
Course logistics and agenda
Module 1: Governance at Scale
Governance at scale focal points
Business and Technical Challenges
Module 2: Governance Automation
Multi-account strategies, guidance, and architecture
Environments for agility and governance at scale
Governance with AWS Control Tower
Use cases for governance at scale
Module 3: Preventive Controls
Enterprise environment challenges for developers
AWS Service Catalog
Resource creation
Workflows for provisioning accounts
Preventive cost and security governance
Self-service with existing IT service management (ITSM) tools
Lab 1: Deploy Resources for AWS Catalog
Create a new AWS Service Catalog portfolio and product.
Add an IAM role to a launch constraint to limit the actions the product can perform.
Grant access for an IAM role to view the catalog items.
Deploy an S3 bucket from an AWS Service Catalog product.
Module 4: Detective Controls
Operations aspect of governance at scale
Resource monitoring
Configuration rules for auditing
Operational insights
Remediation
Clean up accounts
Lab 2: Compliance and Security Automation with AWS Config
Apply Managed Rules through AWS Config to selected resources
Automate remediation based on AWS Config rules
Investigate the Amazon Config dashboard and verify resources and rule compliance
Lab 3: Taking Action with AWS Systems Manager
Setup Resource Groups for various resources based on common requirements
Perform automated actions against targeted Resource Groups
Module 5: Resources
Explore additional resources for security governance at scale
AMA14 - AWS Technical Essentials
Длительность: 1 день (8 часов)
Код курса: АМА14
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Course description
AWS Technical Essentials introduces you to essential AWS services and common solutions. The course covers the fundamental AWS concepts related to compute, database, storage, networking, monitoring, and security. You will start working in AWS through hands-on course experiences. The course covers the concepts necessary to increase your understanding of AWS services, so that you can make informed decisions about solutions that meet business requirements. Throughout the course, you will gain information on how to build, compare, and apply highly available, fault tolerant, scalable, and costeffective cloud solutions.
Activities
This course includes presentations, hands-on labs, demonstrations, videos, and knowledge checks.
Course objectives
In this course, you will learn to:
Describe terminology and concepts related to AWS services
Navigate the AWS Management Console
Articulate key concepts of AWS security measures and AWS Identity and Access Management (IAM)
Distinguish among several AWS compute services, including Amazon Elastic Compute Cloud
(Amazon EC2), AWS Lambda, Amazon Elastic Container Service (Amazon ECS), and Amazon Elastic
Kubernetes Service (Amazon EKS)
Understand AWS database and storage offerings, including Amazon Relational Database Service
(Amazon RDS), Amazon DynamoDB, and Amazon Simple Storage Service (Amazon S3)
Explore AWS networking services
Access and configure Amazon CloudWatch monitoring features
Intended audience
This course is intended for:
Individuals responsible for articulating the technical benefits of AWS services to customers
Individuals interested in learning how to get started with AWS
SysOps administrators
Solutions architects
Developers
Prerequisites
We recommend that attendees of this course have:
IT experience
Basic knowledge of common data center architectures and components (servers, networking,
databases, applications, and so on)
No prior cloud computing or AWS experience required
Enroll today
Visit aws.training to find a class today.
Course outline
Course Introduction
Module 1: Introduction to Amazon Web Services
Introduction to AWS Cloud
Security in the AWS Cloud
Hosting the employee directory application in AWS
Hands-On Lab: Introduction to AWS Identity and Access Management (IAM)
Module 2: AWS Compute
Compute as a service in AWS
Introduction to Amazon Elastic Compute Cloud
Amazon EC2 instance lifecycle
AWS container services
What is serverless?
Introduction to AWS Lambda
Choose the right compute service
Hands-On Lab: Launch the Employee Directory Application on Amazon EC2
Module 3: AWS Networking
Networking in AWS
Introduction to Amazon Virtual Private Cloud (Amazon VPC)
Amazon VPC routing
Amazon VPC security
Hands-On Lab: Create a VPC and Relaunch the Corporate Directory Application in Amazon
EC2
Module 4: AWS Storage
AWS storage types
Amazon EC2 instance storage and Amazon Elastic Block Store (Amazon EBS)
Object storage with Amazon S3
Choose the right storage service
Hands-On Lab: Create an Amazon S3 Bucket
Module 5: Databases
Explore databases in AWS
Amazon Relational Database Service
Purpose-built databases
Introduction to Amazon DynamoDB
Choose the right AWS database service
Hands-On Lab: Implement and manage Amazon DynamoDB
Module 6: Monitoring, Optimization, and Serverless
Monitoring
Optimization
Alternate serverless employee directory application architecture
Hands-On Lab: Configure High Availability for Your Application
Module 7: Course Summary
AMA15 - AWS Well-Architected Best Practices
Длительность: 1 день (8 часов)
Код курса: АМА15
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Course description
The AWS Well-Architected Framework helps you make informed decisions about your customers’ architectures cloud-focused way and understand the impact of design decisions. By using the WellArchitected Framework, you will understand the risks in your architecture and ways to mitigate them. This course provides a deep dive into the AWS Well-Architected Framework and its six pillars. This course also covers the Well-Architected review process and using the AWS Well-Architected Tool to complete reviews.
Activities
This course includes presentations, case studies, hands-on labs, and knowledge checks.
Course objectives
In this course, you will learn to:
Identify the AWS Well-Architected Framework features, design principles, design pillars, and common uses
Apply the design principles, key services, and best practices for each pillar of the WellArchitected Framework
Use the AWS Well-Architected Tool to conduct Well-Architected reviews
Intended audience
This course is intended for:
Technical professionals involved in architecting, building, and operating AWS solutions.
Prerequisites
We recommend that attendees of this course have:
Knowledge of core AWS services (Course: AWS Cloud Practitioner Essentials)
Knowledge of AWS management interfaces (Course: AWS Technical Essentials)
Knowledge of core AWS design and architecture (Course: Architecting on AWS)
Course outline
Module 1: Well-Architected Introduction
Brief history of AWS Well-Architected
AWS Well-Architected pillars
Design principles
Applying the AWS Well-Architected Framework
AWS Well-Architected Tool
Module 2: Operational Excellence
Operational Excellence design principles
Case study
Hands-On Lab: Operational Excellence
Module 3: Reliability
Reliability design principles
Hands-On Lab: Reliability
Module 4: Security
Security design principles
Hands-On Lab: Security
Module 5: Performance Efficiency
Performance Efficiency design principles
Hands-On Lab: Performance Efficiency
Module 6: Cost Optimization
Cost Optimization design principles
Hands-On Lab: Cost Optimization
Module 7: Sustainability
Sustainability design principles
Sustainability best practices
Sustainability pillar resources
Module 8: Course Summary
Recap
Resources
Continue your learning
AMA17 - Building Batch Data Analytics Solutions on AWS
Длительность: 1 день (8 часов)
Код курса: АМА17
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Course description
In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade
Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with
open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and
AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing
components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both
analytics and machine learning workloads. You will also learn to apply security, performance, and cost
management best practices to the operation of Amazon EMR.
Activities
This course includes presentations, interactive demos, practice labs, discussions, and class exercises.
Course objectives
In this course, you will learn to:
Compare the features and benefits of data warehouses, data lakes, and modern data architectures
Design and implement a batch data analytics solution
Identify and apply appropriate techniques, including compression, to optimize data storage
Select and deploy appropriate options to ingest, transform, and store data
Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
Secure data at rest and in transit
Monitor analytics workloads to identify and remediate problems
Apply cost management best practices
Intended audience
This course is intended for:
Data platform engineers
Architects and operators who build and manage data analytics pipelines
Prerequisites
Students with a minimum one-year experience managing open-source data frameworks such as Apache
Spark or Apache Hadoop will benefit from this course.
We suggest the AWS Hadoop Fundamentals course for those that need a refresher on Apache Hadoop.
We recommend that attendees of this course have:
Completed either AWS Technical Essentials or Architecting on AWS
Completed either Building Data Lakes on AWS or Getting Started with AWS Glue
Enroll today
Visit aws.training to find a class today
Course outline
Module A: Overview of Data Analytics and the Data Pipeline
Data analytics use cases
Using the data pipeline for analytics
Module 1: Introduction to Amazon EMR
Using Amazon EMR in analytics solutions
Amazon EMR cluster architecture
Interactive Demo 1: Launching an Amazon EMR cluster
Cost management strategies
Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage
Storage optimization with Amazon EMR
Data ingestion techniques
Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR
• Apache Spark on Amazon EMR use cases
• Why Apache Spark on Amazon EMR
Spark concepts
Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the
Spark shell
Transformation, processing, and analytics
Using notebooks with Amazon EMR
Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR
Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive
Using Amazon EMR with Hive to process batch data
Transformation, processing, and analytics
Practice Lab 2: Batch data processing using Amazon EMR with Hive
Introduction to Apache HBase on Amazon EMR
Module 5: Serverless Data Processing
Serverless data processing, transformation, and analytics
Using AWS Glue with Amazon EMR workloads
Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions
Module 6: Security and Monitoring of Amazon EMR Clusters
Securing EMR clusters
Interactive Demo 3: Client-side encryption with EMRFS
Monitoring and troubleshooting Amazon EMR clusters
Demo: Reviewing Apache Spark cluster history
Module 7: Designing Batch Data Analytics Solutions
Batch data analytics use cases
Activity: Designing a batch data analytics workflow
Module B: Developing Modern Data Architectures on AWS
Modern data architectures
AMA18 - Building Data Analytics Solutions Using Amazon Redshift
Длительность: 1 день (8 часов)
Код курса: АМА18
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Course description
In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse
service. The course focuses on the data collection, ingestion, cataloging, storage, and processing
components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to
support both analytics and machine learning workloads. You will also learn to apply security, performance,
and cost management best practices to the operation of Amazon Redshift.
Activities
This course includes presentations, interactive demos, practice labs, discussions, and class exercises.
Course objectives
In this course, you will learn to:
Compare the features and benefits of data warehouses, data lakes, and modern data architectures
Design and implement a data warehouse analytics solution
Identify and apply appropriate techniques, including compression, to optimize data storage
Select and deploy appropriate options to ingest, transform, and store data
Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
Secure data at rest and in transit
Monitor analytics workloads to identify and remediate problems
Apply cost management best practices
Intended audience
This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
Prerequisites
Students with a minimum one-year experience managing data warehouses will benefit from this course.
We recommend that attendees of this course have:
Completed either AWS Technical Essentials or Architecting on AWS
Completed Building Data Lakes on AWS
Enroll today
Visit aws.training to find a class today.
Course outline
Module A: Overview of Data Analytics and the Data Pipeline
Data analytics use cases
Using the data pipeline for analytics
Module 1: Using Amazon Redshift in the Data Analytics Pipeline
Why Amazon Redshift for data warehousing?
Overview of Amazon Redshift
Module 2: Introduction to Amazon Redshift
Amazon Redshift architecture
Interactive Demo 1: Touring the Amazon Redshift console
Amazon Redshift features
Practice Lab 1: Load and query data in an Amazon Redshift cluster
Module 3: Ingestion and Storage
Ingestion
Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
Data distribution and storage
Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
Querying data in Amazon Redshift
Practice Lab 2: Data analytics using Amazon Redshift Spectrum
Module 4: Processing and Optimizing Data
Data transformation
Advanced querying
Practice Lab 3: Data transformation and querying in Amazon Redshift
Resource management
Interactive Demo 4: Applying mixed workload management on Amazon Redshift
Automation and optimization
Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster
Module 5: Security and Monitoring of Amazon Redshift Clusters
Securing the Amazon Redshift cluster
Monitoring and troubleshooting Amazon Redshift clusters
Module 6: Designing Data Warehouse Analytics Solutions
Data warehouse use case review
Activity: Designing a data warehouse analytics workflow
Module B: Developing Modern Data Architectures on AWS
Modern data architectures
AMA20 - Building Streaming Data Analytics Solutions on AWS
Длительность: 1 день (8 часов)
Код курса: АМА20
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Course description
In this course, you will learn to build streaming data analytics solutions using AWS services, including
Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (Amazon MSK). Amazon Kinesis is a massively scalable and durable real-time data streaming service. Amazon MSK offers a secure, fully managed, and highly available Apache Kafka service. You will learn how Amazon Kinesis and Amazon MSK integrate with AWS services such as AWS Glue and AWS Lambda. The course addresses the streaming data
ingestion, stream storage, and stream processing components of the data analytics pipeline. You will also learn to apply security, performance, and cost management best practices to the operation of Kinesis and Amazon MSK.
Activities
This course includes presentations, practice labs, discussions, and class exercises.
Course objectives
In this course, you will learn to:
Understand the features and benefits of a modern data architecture. Learn how AWS streaming services fit into a modern data architecture.
Design and implement a streaming data analytics solution
Identify and apply appropriate techniques, such as compression, sharding, and partitioning, to optimize data storage
Select and deploy appropriate options to ingest, transform, and store real-time and near real-time data
Choose the appropriate streams, clusters, topics, scaling approach, and network topology for a particular business use case
Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
Secure streaming data at rest and in transit
Monitor analytics workloads to identify and remediate problems
Apply cost management best practices
Intended audience
This course is intended for:
Data engineers and architects
Developers who want to build and manage real-time applications and streaming data analytics solutions
AWS Classroom Training
© 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Prerequisites
We recommend that attendees of this course have:
At least one year of data analytics experience or direct experience building real-time applications or streaming analytics solutions.
We suggest the Streaming Data Solutions on AWS whitepaper for those that need a refresher on streaming concepts.
Completed either Architecting on AWS or Data Analytics Fundamentals
Completed Building Data Lakes on AWS
Enroll today
Visit aws.training to find a class today
Course outline
Module A: Overview of Data Analytics and the Data Pipeline
Data analytics use cases
Using the data pipeline for analytics
Module 1: Using Streaming Services in the Data Analytics Pipeline
The importance of streaming data analytics
The streaming data analytics pipeline
Streaming concepts
Module 2: Introduction to AWS Streaming Services
Streaming data services in AWS
Amazon Kinesis in analytics solutions
Demonstration: Explore Amazon Kinesis Data Streams
Practice Lab: Setting up a streaming delivery pipeline with Amazon Kinesis
Using Amazon Kinesis Data Analytics
Introduction to Amazon MSK
Overview of Spark Streaming
Module 3: Using Amazon Kinesis for Real-time Data Analytics
Exploring Amazon Kinesis using a clickstream workload
Creating Kinesis data and delivery streams
Demonstration: Understanding producers and consumers
Building stream producers
Building stream consumers
Building and deploying Flink applications in Kinesis Data Analytics
Demonstration: Explore Zeppelin notebooks for Kinesis Data Analytics
Practice Lab: Streaming analytics with Amazon Kinesis Data
Analytics and Apache Flink
Module 4: Securing, Monitoring, and Optimizing Amazon Kinesis
Optimize Amazon Kinesis to gain actionable business insights
Security and monitoring best practices
Module 5: Using Amazon MSK in Streaming Data Analytics Solutions
Use cases for Amazon MSK
Creating MSK clusters
Demonstration: Provisioning an MSK Cluster
Ingesting data into Amazon MSK
Practice Lab: Introduction to access control with Amazon MSK
Transforming and processing in Amazon MSK
Module 6: Securing, Monitoring, and Optimizing Amazon MSK
Optimizing Amazon MSK
Demonstration: Scaling up Amazon MSK storage
Practice Lab: Amazon MSK streaming pipeline and application deployment
Security and monitoring
Demonstration: Monitoring an MSK cluster
Module 7: Designing Streaming Data Analytics Solutions
Use case review
Class Exercise: Designing a streaming data analytics workflow
Module B: Developing Modern Data Architectures on AWS
Modern data architectures
AMA22 - Data Warehousing on AWS
Длительность: 3 дня (24 часа)
Код курса: АМА22
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Course description
Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloudbased data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course
demonstrates how to use Amazon QuickSight to perform analysis on your data.
Activities
This course includes presentations, group exercises, and hands-on labs.
Course objectives
In this course, you will:
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
Data Scientists
Prerequisites
We recommend that attendees of this course have:
Taken AWS Technical Essentials (or equivalent experience with AWS)
Familiarity with relational databases and database design concepts
Enroll today
Visit aws.training to find a class today.
Course outline
Day 1
Module 1: Introduction to Data Warehousing
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 2
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 3
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
AMA23 - Developing on AWS
Длительность: 3 дня (24 часа)
Код курса: АМА23
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Course description
This course teaches experienced developers how to programmatically interact with AWS services to build
web solutions. It guides you through a high-level architectural discussion on resource selection and dives
deep into using the AWS Software Development Kits (AWS SDKs) and Command Line Interface (AWS CLI)
to build and deploy your cloud applications. You will build a sample application during this course, learning
how to set up permissions to the development environment, adding business logic to process data using
AWS core services, configure user authentications, deploy to AWS cloud, and debug to resolve application
issues. The course includes code examples to help you implement the design patterns and solutions
discussed in the course. The labs reinforce key course content and help you to implement solutions using
the AWS SDK for Python, .Net, and Java, the AWS CLI, and the AWS Management Console.
Activities
This course includes presentations, demonstrations, and hands-on labs.
Course objectives
In this course, you will learn to:
Build a simple end-to-end cloud application using AWS Software Development Kits (AWS SDKs),
Command Line Interface (AWS CLI), and IDEs.
Configure AWS Identity and Access Management (IAM) permissions to support a development environment.
Use multiple programming patterns in your applications to access AWS services.
Use AWS SDKs to perform CRUD (create, read, update, delete) operations on Amazon Simple
Storage Service (Amazon S3) and Amazon DynamoDB resources.
Build AWS Lambda functions with other service integrations for your web applications.
Understand the benefits of microservices architectures and serverless applications to design.
Develop API Gateway components and integrate with other AWS services.
Explain how Amazon Cognito controls user access to AWS resources.
Build a web application using Cognito to provide and control user access.
Use DevOps methodology to reduce the risks associated with traditional application releases and identify AWS services that help in implementing DevOps practices.
Use AWS Serverless Application Model (AWS SAM) to deploy an application.
Observe your application build using Amazon X-Ray.
Intended audience
This course is intended for experienced:
Software developers
Solution architects
IT workers who want to improve their developing skills using AWS Services
Prerequisites
We recommend that attendees of this course have:
AWS Technical Essentials
Working knowledge of AWS core services
Programming experience in any one of the following languages:
Python
NET
Java
Enroll today
Visit aws.training to find a class today
Course outline
Day 1
Module 1: Course Overview
Logistics
Student resources
Agenda
Introductions
Module 2: Building a Web Application on AWS
Discuss the architecture of the application you are going to build during this course
Explore the AWS services needed to build your web application
Discover how to store, manage, and host your web application
Module 3: Getting Started with Development on AWS
Describe how to access AWS services programmatically
List some programmatic patterns and how they provide efficiencies within AWS SDKs and AWS CLI
Explain the value of AWS Cloud9
Module 4: Getting Started with Permissions
Review AWS Identity and Access Management (IAM) features and components permissions
to support a development environment
Demonstrate how to test AWS IAM permissions
Configure your IDEs and SDKs to support a development environment
Demonstrate accessing AWS services using SDKs and AWS Cloud9
Lab 1: Configure the Developer Environment
Connect to a developer environment
Verify that the IDE and the AWS CLI are installed and configured to use the application profile
Verify that the necessary permissions have been granted to run AWS CLI commands
Assign an AWS IAM policy to a role to delete an Amazon S3 bucket
Module 5: Getting Started with Storage
Describe the basic concepts of Amazon S3
List the options for securing data using Amazon S3
Define SDK dependencies for your code
Explain how to connect to the Amazon S3 service
Describe request and response objects
Module 6: Processing Your Storage Operations
Perform key bucket and object operations
Explain how to handle multiple and large objects
Create and configure an Amazon S3 bucket to host a static website
Grant temporary access to your objects
Demonstrate performing Amazon S3 operations using SDKs
Lab 2: Develop Solutions Using Amazon S3
Interact with Amazon S3 programmatically using AWS SDKs and the AWS CLI
Create a bucket using waiters and verify service exceptions codes
Build the needed requests to upload an Amazon S3 object with metadata attached
Build requests to download an object from the bucket, process data, and upload the object back to the bucket
Configure a bucket to host the website and sync the source files using the AWS CLI
Add IAM bucket policies to access the S3 website.
Day 2
Module 7: Getting Started with Databases
Describe the key components of DynamoDB
Explain how to connect to DynamoDB
Describe how to build a request object
Explain how to read a response object
List the most common troubleshooting exceptions
Module 8: Processing Your Database Operations
Develop programs to interact with DynamoDB using AWS SDKs
Perform CRUD operations to access tables, indexes, and data
Describe developer best practices when accessing DynamoDB
Review caching options for DynamoDB to improve performance
Perform DynamoDB operations using SDK
Lab 3: Develop Solutions Using Amazon DynamoDB
Interact with Amazon DynamoDB programmatically using low-level, document, and highlevel APIs in your programs
Retrieve items from a table using key attributes, filters, expressions, and paginations
Load a table by reading JSON objects from a file
Search items from a table based on key attributes, filters, expressions, and paginations
Update items by adding new attributes and changing data conditionally
Access DynamoDB data using PartiQL and object-persistence models where applicable
Module 9: Processing Your Application Logic
Develop a Lambda function using SDKs
Configure triggers and permissions for Lambda functions
Test, deploy, and monitor Lambda functions
Lab 4: Develop Solutions Using AWS Lambda Functions
Create AWS Lambda functions and interact programmatically using AWS SDKs and AWS CLI
Configure AWS Lambda functions to use the environment variables and to integrate with other services
Generate Amazon S3 pre-signed URLs using AWS SDKs and verify the access to bucket objects
Deploy the AWS Lambda functions with .zip file archives through your IDE and test as needed
Invoke AWS Lambda functions using the AWS Console and AWS CLI
Module 10: Managing the APIs
Describe the key components of API Gateway
Develop API Gateway resources to integrate with AWS services
Configure API request and response calls for your application endpoints
Test API resources and deploy your application API endpoint
Demonstrate creating API Gateway resources to interact with your application APIs
Lab 5: Develop Solutions Using Amazon API Gateway
Create RESTful API Gateway resources and configure CORS for your application
Integrate API methods with AWS Lambda functions to process application data
Configure mapping templates to transform the pass-through data during method integration
Create a request model for API methods to ensure that the pass-through data format complies with application rules
Deploy the API Gateway to a stage and validate the results using the API endpoint
AMA24 - Developing Serverless Solutions on AWS
Длительность: 3 дня (24 часа)
Код курса: АМА24
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Course description
This course gives developers exposure to and practice with best practices for building serverless
applications using AWS Lambda and other services in the AWS serverless platform. You will use AWS
frameworks to deploy a serverless application in hands-on labs that progress from simpler to more
complex topics. You will use AWS documentation throughout the course to develop authentic methods for
learning and problem-solving beyond the classroom.
Activities
This course includes presentations, hands-on labs, demonstrations, videos, knowledge checks, and group
exercises.
Course objectives
In this course, you will learn to:
Apply event-driven best practices to a serverless application design using appropriate AWS services
Identify the challenges and trade-offs of transitioning to serverless development, and make recommendations that suit your development organization and environment
Build serverless applications using patterns that connect AWS managed services together, and account for service characteristics, including service quotas, available integrations, invocation model, error handling, and event source payload
Compare and contrast available options for writing infrastructure as code, including AWS
CloudFormation, AWS Amplify, AWS Serverless Application Model (AWS SAM), and AWS Cloud
Development Kit (AWS CDK)
Apply best practices to writing Lambda functions inclusive of error handling, logging, environment
re-use, using layers, statelessness, idempotency, and configuring concurrency and memory
Apply best practices for building observability and monitoring into your serverless application
Apply security best practices to serverless applications
Identify key scaling considerations in a serverless application, and match each consideration to the
methods, tools, or best practices to manage it
Use AWS SAM, AWS CDK, and AWS developer tools to configure a CI/CD workflow, and automate
deployment of a serverless application
Create and actively maintain a list of serverless resources that will assist in your ongoing serverless
development and engagement with the serverless community
Intended audience
This course is intended for:
Developers who have some familiarity with serverless and experience with development in the AWS Cloud
Prerequisites
We recommend that attendees of this course have:
Familiarity with the basics of AWS Cloud architecture
An understanding of developing applications on AWS equivalent to completing the Developing on
AWS classroom training
Knowledge equivalent to completing the following serverless digital trainings: AWS Lambda
Foundations and Amazon API Gateway for Serverless Applications
Enroll today
Visit aws.training to find a class today
Course outline
Day 1
Module 0: Introduction
Introduction to the application you will build
Access to course resources (Student Guide, Lab Guide, and Online Course Supplement)
Module 1: Thinking Serverless
Best practices for building modern serverless applications
Event-driven design
AWS services that support event-driven serverless applications
Module 2: API-Driven Development and Synchronous Event Sources
Characteristics of standard request/response API-based web applications
How Amazon API Gateway fits into serverless applications
Try-it-out exercise: Set up an HTTP API endpoint integrated with a Lambda function
High-level comparison of API types (REST/HTTP, WebSocket, GraphQL)
Module 3: Introduction to Authentication, Authorization, and Access Control
Authentication vs. Authorization
Options for authenticating to APIs using API Gateway
Amazon Cognito in serverless applications
Amazon Cognito user pools vs. federated identities
Module 4: Serverless Deployment Frameworks
Overview of imperative vs. declarative programming for infrastructure as code
Comparison of CloudFormation, AWS CDK, Amplify, and AWS SAM frameworks
Features of AWS SAM and the AWS SAM CLI for local emulation and testing
Module 5: Using Amazon EventBridge and Amazon SNS to Decouple Components
Development considerations when using asynchronous event sources
Features and use cases of Amazon EventBridge
Try-it-out exercise: Build a custom EventBridge bus and rule
Comparison of use cases for Amazon Simple Notification Service (Amazon SNS) vs. EventBridge
Try-it-out exercise: Configure an Amazon SNS topic with filtering
Module 6: Event-Driven Development Using Queues and Streams
Development considerations when using polling event sources to trigger Lambda functions
Distinctions between queues and streams as event sources for Lambda
Selecting appropriate configurations when using Amazon Simple Queue Service (Amazon
SQS) or Amazon Kinesis Data Streams as an event source for Lambda
Try-it-out exercise: Configure an Amazon SQS queue with a dead-letter queue as a Lambda event source
Hands-On Labs
Hands-On Lab 1: Deploying a Simple Serverless Application
Hands-On Lab 2: Message Fan-Out with Amazon EventBridge
Day 2
Module 7: Writing Good Lambda Functions
How the Lambda lifecycle influences your function code
Best practices for your Lambda functions
Configuring a function
Function code, versions and aliases
Try-it-out exercise: Configure and test a Lambda function
Lambda error handling
Handling partial failures with queues and streams
Module 8: Step Functions for Orchestration
AWS Step Functions in serverless architectures
Try-it-out exercise: Step Functions states
The callback pattern
Standard vs. Express Workflows
Step Functions direct integrations
Try-it-out exercise: Troubleshooting a Standard Step Functions workflow
Module 9: Observability and Monitoring
The three pillars of observability
Amazon CloudWatch Logs and Logs Insights
Writing effective log files
Try-it-out exercise: Interpreting logs
Using AWS X-Ray for observability
Try-it-out exercise: Enable X-Ray and interpret X-Ray traces
CloudWatch metrics and embedded metrics format
Try-it-out exercise: Metrics and alarms
Try-it-out exercise: ServiceLens
Hands-On Labs
Hands-On Lab 3: Workflow Orchestration Using AWS Step Functions
Hands-On Lab 4: Observability and Monitoring
Day 3
Module 10: Serverless Application Security
Security best practices for serverless applications
Applying security at all layers
API Gateway and application security
Lambda and application security
Protecting data in your serverless data stores
Auditing and traceability
Module 11: Handling Scale in Serverless Applications
Scaling considerations for serverless applications
Using API Gateway to manage scale
Lambda concurrency scaling
How different event sources scale with Lambda
Module 12: Automating the Deployment Pipeline
The importance of CI/CD in serverless applications
Tools in a serverless pipeline
AWS SAM features for serverless deployments
Best practices for automation
Course wrap-up
Hands-On Labs
Hands-On Lab 5: Securing Serverless Applications
Hands-On Lab 6: Serverless CI/CD on AWS
AMA25 - DevOps Engineering on AWS
Длительность: 3 дня (24 часа)
Код курса: АМА25
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Course description
DevOps Engineering on AWS teaches you how to use the combination of DevOps cultural philosophies,
practices, and tools to increase your organization’s ability to develop, deliver, and maintain applications and
services at high velocity on AWS. This course covers Continuous Integration (CI), Continuous Delivery (CD),
infrastructure as code, microservices, monitoring and logging, and communication and collaboration.
Hands-on labs give you experience building and deploying AWS CloudFormation templates and CI/CD
pipelines that build and deploy applications on Amazon Elastic Compute Cloud (Amazon EC2), serverless
applications, and container-based applications. Labs for multi-pipeline workflows and pipelines that deploy
to multiple environments are also included.
Activities
This course includes presentations, group exercises, and hands-on labs.
Course objectives
In this course, you will:
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
Enroll today
Visit aws.training to find a class today
Course outline
Day 1
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 2
Module 4: Continuous integration and continuous delivery (CI/CD) with development tools
AWS CodePipeline
Demonstration: AWS integration with Jenkins
Hands-on lab: Automating code deployments using AWS CodePipeline
Module 5: Introduction to Microservices
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
Day 3
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
AMA26 - Migrating to AWS
Длительность: 3 дня (24 часа)
Код курса: АМА26
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Course description
This course is for individuals who seek an understanding of how to plan and migrate existing workloads to
the AWS Cloud. You will learn about various cloud migration strategies and how to apply each step of the
migration process, including portfolio discovery, application migration planning and design, conducting a
migration, and post-migration validation and application optimization. Hands-on labs reinforce learning,
and each lab is designed to provide you with the understanding and foundation necessary to complete
migration tasks in your organization.
Activities
This course includes presentations, hands-on labs, demonstrations, assessments, and group exercises.
Course objectives
In this course, you will learn to:
Recognize the common business and technical drivers for migrating to the cloud
Summarize the three phases of a migration and associated objectives, tasks, and stakeholders for each
Describe AWS architecture, tools, and migration best practices
Distinguish between the various cloud migration strategies and when each is most appropriate
Determine an organization’s application migration readiness
Discover a portfolio and gather data necessary for migration
Plan and design an application migration strategy
Perform and validate application migration to the cloud
Optimize applications and operations after migrating to the cloud
Intended audience
This course is intended for:
Solutions architects
Software engineers
IT project managers
Operation leads
Other individuals involved in the planning and running of migration projects
Prerequisites
We recommend that attendees of this course have:
Familiarity with enterprise IT infrastructure (hardware and software)
Completed the AWS Technical Essentials or Architecting on AWS classroom training
Achieved their AWS Certified Solutions Architect – Associate certification (optional)
Enroll today
Visit aws.training to find a class today
Course outline
Day 1
Module 1: What Is a Cloud Migration?
Drivers and outcomes of a cloud migration
Planning for a successful cloud migration
The three-phase migration process
Module 2: Assessing Migration Readiness
The assess phase
Cloud readiness assessment tools
Examine your IT landscape and build your business case
Group Exercise: The Cloud Adoption Tool (CART)
Module 3: Preparing for a Migration and Understanding Related Workstreams
Mobilize phase
Migration-related workstreams
Module 4: Discovering Landing Zones and Their Benefits
What is a landing zone?
Custom multi-account structure with AWS Organizations
AWS Control Tower
Customizations for AWS Control Tower (CfCt)
Planning for connectivity
Module 5: Building a Landing Zone
Planning a landing zone
Design a multi-account structure
Governance polices
Planning for connectivity
Demonstration: AWS Control Tower
Hands-On Lab: Connecting Your On-Premises Network and Directory Services to AWS
Module 6: Discovering the Portfolio and Understanding Migration Strategies
Detailed portfolio discovery workstream
Evaluating cloud readiness
Cloud migration strategies
Group Exercise: Choose a migration strategy (scenario-based)
Day 2
Module 7: Understanding and Choosing Portfolio Discovery Tools
Migration Evaluator
AWS Migration Hub and AWS Application Discovery Service
AWS Systems Manager and Amazon CloudWatch
Hands-On Lab: Gathering Application Data Necessary for Migration
Module 8: Planning and Designing a Migration
Plan the migration overall
Building the migration factory
Design the migration for each application
Group Exercise: Build a migration plan
Group Exercise: Design for migration
Module 9: Performing the Migration to AWS
Server migration process
Server migration tools
VMware Cloud on AWS
AWS Migration Hub
AWS Application Migration Service (AWS MGN)
Evaluating server migration tools
Hands-On Lab: Migrating an Application to AWS
Module 10: Understanding Database and Data Migration Services
Data migration
Online data migration services
Offline data migration services
Database migration
Hands-On Lab: Migrating an Existing Database to Amazon Aurora
Day 3
Module 11: Understanding Additional Migration Support Options
AWS Managed Services
AWS Service Catalog
AWS Service Catalog integrations
Microsoft workloads on AWS
SAP on AWS
Module 12: Integrating, Validating, and Cutting Over Applications
Migrate and modernize phase
Cutover strategy
Module 13: Modernizing and Optimizing Applications
Cost optimization
Performance optimization
AWS tools used to optimize
Modernize the enterprise
Use containers
Use serverless architectures
Hands-On Lab: Optimizing an Application with Amazon S3 and Amazon ECS
Module 14: Understanding Operations Tools, Integration Testing, and Automation
AWS Config
Infrastructure and operations as code
Adopting a DevOps approach
Automate change and configuration
Automate management
Module 15: Migration Best Practices
Course review
Best practices
Continue your learning
Hands-On Lab: Automating Application Deployments
AMA27 - MLOps Engineering on AWS
Длительность: 3 дня (24 часа)
Код курса: АМА27
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Course description
This course builds upon and extends the DevOps methodology prevalent in software development to
build, train, and deploy machine learning (ML) models. The course is based on the four-level MLOPs
maturity framework. The course focuses on the first three levels, including the initial, repeatable, and
reliable levels. The course stresses the importance of data, model, and code to successful ML
deployments. It demonstrates the use of tools, automation, processes, and teamwork in addressing the
challenges associated with handoffs between data engineers, data scientists, software developers, and
operations. The course also discusses the use of tools and processes to monitor and take action when
the model prediction in production drifts from agreed-upon key performance indicators.
Activities
This course includes presentations, hands-on labs, demonstrations, knowledge checks, and workbook activities.
Course objectives
In this course, you will learn to:
Explain the benefits of MLOps
Compare and contrast DevOps and MLOps
Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies
Set up experimentation environments for MLOps with Amazon SageMaker
Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code)
Describe three options for creating a full CI/CD pipeline in an ML context
Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code)
Demonstrate how to monitor ML based solutions
Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion; detects performance degradation; and re-trains the model on top ofnewly acquired data
Intended audience
This course is intended for:
MLOps engineers who want to productionize and monitor ML models in the AWS cloud
DevOps engineers who will be responsible for successfully deploying and maintaining ML
models in production
Prerequisites
We recommend that attendees of this course have:
AWS Technical Essentials (classroom or digital)
DevOps Engineering on AWS, or equivalent experience
Practical Data Science with Amazon SageMaker, or equivalent experience
Course outline
Day 1
Module 1: Introduction to MLOps
Processes
People
Technology
Security and governance
MLOps maturity model
Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio
Bringing MLOps to experimentation
Setting up the ML experimentation environment
Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio
Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog
Workbook: Initial MLOps
Module 3: Repeatable MLOps: Repositories
Managing data for MLOps
Version control of ML models
Code repositories in ML
Module 4: Repeatable MLOps: Orchestration
ML pipelines
Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines
Day 2
Module 4: Repeatable MLOps: Orchestration (continued)
End-to-end orchestration with AWS Step Functions
Hands-On Lab: Automating a Workflow with Step Functions
End-to-end orchestration with SageMaker Projects
Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects
Using third-party tools for repeatability
Demonstration: Exploring Human-in-the-Loop During Inference
Governance and security
Demonstration: Exploring Security Best Practices for SageMaker
Workbook: Repeatable MLOps
Module 5: Reliable MLOps: Scaling and Testing
Scaling and multi-account strategies
Testing and traffic-shifting
Demonstration: Using SageMaker Inference Recommender
Hands-On Lab: Testing Model Variants
Day 3
Module 5: Reliable MLOps: Scaling and Testing (continued)
Hands-On Lab: Shifting Traffic
Workbook: Multi-account strategies
Module 6: Reliable MLOps: Monitoring
The importance of monitoring in ML
Hands-On Lab: Monitoring a Model for Data Drift
Operations considerations for model monitoring
Remediating problems identified by monitoring ML solutions
Workbook: Reliable MLOps
Hands-On Lab: Building and Troubleshooting an ML Pipeline
AMA28 - Networking Essentials for Cloud Applications on AWS
Длительность: 1 день (8 часов)
Код курса: АМА28
Стоимость обучения.
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Course description
The Networking Essentials for Cloud Applications on AWS course provides a comprehensive
understanding of networking concepts and services within the Amazon Web Services (AWS) cloud
environment. Designed for novice and experienced networking engineers, this course covers essential
topics, best practices, and hands-on labs. Its purpose is to equip learners with the knowledge and skills
that are required to design, configure, and optimize network infrastructure on AWS.
Activities
This course includes presentations, demonstrations, knowledge checks, and three hands-on labs that
revolve around a use case story.
Course objectives
In this course, you will learn to:
Design a networking infrastructure for a scalable production application, considering design trade-offs between different networking services.
Configure networking services for a highly available, resilient, and scalable application.
Implement the networking infrastructure according to evolving business requirements.
Implement networking best practices to align towards AWS Well-Architected Framework.
Intended audience
This course is intended for:
Newly hired cloud engineers
On-premises IT engineers
Cloud architects
Cloud engineers
Network engineers
Prerequisites
We recommend that attendees of this course have:
Basic knowledge of networking concepts
Basic knowledge of AWS services
AWS Technical Essentials or Cloud Practitioner Essentials
Course outline
Module 0: Course Introduction
Introductions
Course overview
Use case introduction
Module 1: Networking on AWS
IP addressing
Amazon Virtual Private Cloud (Amazon VPC) fundamentals
Subnets
Amazon VPC IP Address Manager (IPAM)
Elastic Network Interfaces
Elastic IP addressing
Route table
Internet and NAT gateways
Basic traffic filtering mechanisms for a VPC
Knowledge check
Module 2: Load Balancing and Scaling on AWS
Elastic Load Balancing (ELB)
Cross-zone load balancing
Auto Scaling group (ASG) basics
Knowledge check
Use case part one
Hands-on lab: Building a Multi-Availability Zone VPC Architecture
Module 3: VPC Interconnectivity and Content Delivery
VPC interconnectivity
VPC peering
VPC Transit Gateway
VPC endpoints
Edge locations
AWS Global Accelerator
Knowledge check
Use case part two
Hands-on lab: Accelerating Performance with Amazon CloudFront
Module 4: High Availability with Amazon Route 53
Amazon Route 53
Knowledge check
Use case part three
Hands-on lab: Achieving Fault Tolerance and Global Traffic Optimization
Module 5: Course Wrap-Up
Course reflection
Use case labs recap
Use case conclusion
Course feedback survey
AMA29 - Planning and Designing Databases on AWS
Длительность: 3 дня (24 часа)
Код курса: АМА29
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Course description
In this course, you will learn about planning and designing your solutions with purpose-built Amazon Web
Services (AWS) Cloud databases. The course introduces you to the features and characteristics of each of these databases and shares the design considerations that you should make while using them. By taking this course, you can develop the analytical skills needed to choose the right AWS database for your unique needs.
By the end of the course, you will be able to analyze a business use case, analyze the workload, and assess application requirements to identify and design the most suitable AWS database solution to support your organizational needs.
Activities
This course provides opportunities for you to apply concepts through various activities. It includes instructorled presentations, demonstrations, individual and group activities, knowledge checks, and hands-on labs to apply concepts.
Course objectives
In this course, you will learn how to do the following:
Summarize the AWS Well-Architected Framework for designing database solutions.
Choose an appropriate purpose-built database service for a given workload.
Design a relational database solution to solve a business problem.
Design a NoSQL database solution to solve a business problem.
Analyze data from multiple databases to solve a business problem.
Discuss the security considerations for your database solution.
Intended audience
This course is intended for learners in the following roles:
Solutions architects
Database architects
Developers
We recommend the following prerequisites for attendees of this course:
Familiarity with AWS database services
Understanding of database design concepts and/or data modeling for relational or nonrelational databases
Familiarity with cloud computing concepts
Familiarity with general networking and encryption concepts
Completion of the digital course Introduction to Building with AWS Databases
Enroll today
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Course outline
Day 1
Module 0: Course Introduction
Course overview
Module 1: AWS Purpose-Built Databases
Discussing well-architected databases
Analyzing workload requirements
Choosing the data model
Choosing the right purpose-built database
Knowledge check
Module 2: Amazon Relational Database Service (Amazon RDS)
Discussing a relational database
What is Amazon RDS?
Why Amazon RDS?
Amazon RDS design considerations
Knowledge check
Module 3: Amazon Aurora
What is Amazon Aurora?
Why Amazon Aurora?
Aurora design considerations
Knowledge check
Challenge Lab 1: Working with Amazon Aurora databases
Day 2
Class Activity 1: Choose the Right Relational Database
Module 4: Amazon DynamoDB
Discussing a key value database
What is DynamoDB?
Why DynamoDB?
DynamoDB design considerations
Knowledge check
Module 5: Amazon Keyspaces (for Apache Cassandra)
Discussing a wide-column database
What is Apache Cassandra?
What is Amazon Keyspaces?
Why Amazon Keyspaces?
Amazon Keyspaces design considerations
Knowledge check
Module 6: Amazon DocumentDB (with MongoDB compatibility)
Discussing a document database
What is Amazon DocumentDB?
Why Amazon DocumentDB?
Amazon DocumentDB design considerations
Knowledge check
Module 7: Amazon Quantum Ledger Database (Amazon QLDB)
Discussing a ledger database
What is Amazon QLDB?
Why Amazon QLDB?
Amazon QLDB design considerations
Knowledge check
Class Activity 2: Choose the Right Nonrelational Database
Challenge Lab 2: Working with Amazon DynamoDB Tables
Day 3
Module 8: Amazon Neptune
Discussing a graph database
What is Amazon Neptune?
Why Amazon Neptune?
Amazon Neptune design considerations
Knowledge check
Module 9: Amazon Timestream
Discussing a timeseries database
What is Amazon Timestream?
Why Amazon Timestream?
Amazon Timestream design considerations
Knowledge check
Module 10: Amazon ElastiCache
Discussing an in-memory database
What is ElastiCache?
Why ElastiCache?
ElastiCache design considerations
Knowledge check
Module 11: Amazon MemoryDB for Redis
What is Amazon MemoryDB (for Redis)?
Why Amazon MemoryDB?
Amazon MemoryDB design considerations
Knowledge check
Class Activity 3: Let’s Cache In
Module 12: Amazon Redshift
Discussing a data warehouse
What is Amazon Redshift?
Why Amazon Redshift?
Amazon Redshift design considerations
Knowledge check
Module 13: Tools for Working with AWS Databases
Data access and analysis with Amazon Athena
Data migration with SCT and DMS
Class Activity 4: Overall Picture
Challenge Lab 3: Working with Amazon Redshift clusters
AMA30 - Practical Data Science with Amazon SageMaker
Длительность: 1 день (8 часов)
Код курса: АМА30
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Course description
Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, you will
spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and
build applications that integrate with ML. You will learn the basic process data scientists use to develop
ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. You will experience the steps to
build, train, and deploy an ML model through instructor-led demonstrations and labs.
Activities
This course includes presentations, hands-on labs, and demonstrations.
Course objectives
In this course, you will learn to:
Discuss the benefits of different types of machine learning for solving business problems
Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
Explain how data scientists use AWS tools and ML to solve a common business problem
Summarize the steps a data scientist takes to prepare data
Summarize the steps a data scientist takes to train ML models
Summarize the steps a data scientist takes to evaluate and tune ML models
Summarize the steps to deploy a model to an endpoint and generate predictions
Describe the challenges for operationalizing ML models
Match AWS tools with their ML function
Intended audience
This course is intended for:
Development Operations (DevOps) engineers
Application developers
Prerequisites
We recommend that attendees of this course have:
AWS Technical Essentials
Entry-level knowledge of Python programming
Entry-level knowledge of statistics
Course outline
Module 1: Introduction to Machine Learning
Benefits of machine learning (ML)
Types of ML approaches
Framing the business problem
Prediction quality
Processes, roles, and responsibilities for ML projects
Module 2: Preparing a Dataset
Data analysis and preparation
Data preparation tools
Demonstration: Review Amazon SageMaker Studio and Notebooks
Hands-On Lab: Data Preparation with SageMaker Data Wrangler
Module 3: Training a Model
Steps to train a model
Choose an algorithm
Train the model in Amazon SageMaker
Hands-On Lab: Training a Model with Amazon SageMaker
Amazon CodeWhisperer
Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks
Module 4: Evaluating and Tuning a Model
Model evaluation
Model tuning and hyperparameter optimization
Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker
Module 5: Deploying a Model
Model deployment
Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction
Module 6: Operational Challenges
Responsible ML
ML team and MLOps
Automation
Monitoring
Updating models (model testing and deployment)
Module 7: Other Model-Building Tools
Different tools for different skills and business needs
No-code ML with Amazon SageMaker Canvas
Demonstration: Overview of Amazon SageMaker Canvas
Amazon SageMaker Studio Lab
Demonstration: Overview of SageMaker Studio Lab
(Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint
AMA31 - Running Containers on Amazon Elastic Kubernetes Service (Amazon EKS)
Длительность: 3 дня (24 часа)
Код курса: АМА31
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Course description
Amazon EKS makes it easy for you to run Kubernetes on AWS without needing to install, operate, and maintain your own Kubernetes control plane. In this course, you will learn container management and
orchestration for Kubernetes using Amazon EKS.
You will build an Amazon EKS cluster, configure the environment, deploy the cluster, and then add applications to your cluster. You will manage container images using Amazon Elastic Container Registry (ECR) and learn how to automate application deployment. You will deploy applications using CI/CD tools. You will learn how to monitor and scale your environment by using metrics, logging, tracing, and
horizontal/vertical scaling. You will learn how to design and manage a large container environment by designing for efficiency, cost, and resiliency. You will configure AWS networking services to support the cluster and learn how to secure your Amazon EKS environment.
Activities
This course includes instructor lecture, presentations, hands-on labs, demonstrations, and group exercises/discussions.
Course objectives
In this course, you will learn to:
Describe Kubernetes and Amazon EKS fundamentals and the impact of containers on workflows.
Build an Amazon EKS cluster by selecting the correct compute resources to support worker nodes.
Secure your environment with AWS Identity and Access Management (IAM) authentication and
Kubernetes Role Based Access Control (RBAC) authorization.
Deploy an application on the cluster. Publish container images to Amazon ECR and secure access via IAM policy.
Deploy applications using automated tools and pipelines. Create a GitOps pipeline using WeaveFlux.
Collect monitoring data through metrics, logs, and tracing with AWS X-Ray and identify metrics for performance tuning. Review scenarios where bottlenecks require the best scaling approach using horizontal or vertical scaling.
Assess the tradeoffs between efficiency, resiliency, and cost and the impact of tuning for one over the others. Describe and outline a holistic, iterative approach to optimizing your environment.
Design for cost, efficiency, and resiliency
Configure AWS networking services to support the cluster. Describe how Amazon Virtual Private
Cloud (VPC) supports Amazon EKS clusters and simplifies inter-node communications. Describe the
function of the VPC Container Network Interface (CNI). Review the benefits of a service mesh.
Upgrade your Kubernetes, Amazon EKS, and third party tools.
Intended audience
This course is intended for people who provide container orchestration management in the AWS Cloud including:DevOps engineers
Systems administrators
Prerequisites
We recommend that attendees of this course have:
Completed Introduction to Containers
Completed Amazon Elastic Kubernetes Service (EKS) Primer
Completed AWS Cloud Practitioner Essentials (or equivalent real-world experience)
Basic Linux administration experience
Basic network administration experience
Basic knowledge of containers and microservices
Course outline
Day 1
Module 0: Course Introduction
Course preparation activities and agenda
Module 1: Kubernetes Fundamentals
Container orchestration
Kubernetes objects
Kubernetes internals
Module 2: Amazon EKS Fundamentals
Introduction to Amazon EKS
Amazon EKS control plane
Amazon EKS data plane
Fundamentals of Amazon EKS security
Amazon EKS API
Module 3: Building an Amazon EKS Cluster
Configuring your environment
Creating an Amazon EKS cluster
Demo: Deploying a cluster
Deploying worker nodes
Demo: Completing a cluster configuration
Preparing for Lab 1: Building an Amazon EKS Cluster
Module 4: Deploying Applications to Your Amazon EKS Cluster
Configuring Amazon Elastic Container Registry (Amazon ECR)
Demo: Configuring Amazon ECR
Deploying applications with Helm
Demo: Deploying applications with Helm
Continuous deployment in Amazon EKS
GitOps and Amazon EKS
Preparing for Lab 2: Deploying Applications
Day 2
Module 5: Configuring Observability in Amazon EKS
Configuring observability in an Amazon EKS cluster
Collecting metrics
Using metrics for automatic scaling
Managing logs
Application tracing in Amazon EKS
Gaining and applying insight from observability
Preparing for Lab 3: Monitoring Amazon EKS
Module 6: Balancing Efficiency, Resilience, and Cost Optimization in Amazon EKS
The high level overview
Designing for resilience
Designing for cost optimization
Designing for efficiency
Module 7: Managing Networking in Amazon EKS
Review: Networking in AWS
Communicating in Amazon EKS
Managing your IP space
Deploying a service mesh
Preparing for Lab 4: Exploring Amazon EKS Communication
Day 3
Module 8: Managing Authentication and Authorization in Amazon EKS
Understanding the AWS shared responsibility model
Authentication and authorization
Managing IAM and RBAC
Demo: Customizing RBAC roles
Managing pod permissions using RBAC service accounts
Module 9: Implementing Secure Workflows
Securing cluster endpoint access
Improving the security of your workflows
Improving host and network security
Managing secrets
Preparing for Lab 5: Securing Amazon EKS
Module 10: Managing Upgrades in Amazon EKS
Planning for an upgrade
Upgrading your Kubernetes version
Amazon EKS platform versions
AMA32 - Security Engineering on AWS
Длительность: 3 дня (24 часа)
Код курса: АМА32
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Course description
Security is a concern for both customers in the cloud, and those considering cloud adoption. An increase in
cyberattacks and data leaks remains top of mind for most industry personnel. The Security Engineering on
AWS course addresses these concerns by helping you better understand how to interact and build with
Amazon Web Services (AWS) in a secure way. In this course, you will learn about managing identities and
roles, managing and provisioning accounts, and monitoring API activity for anomalies. You will also learn
about how to protect data stored on AWS. The course explores how you can generate, collect, and monitor
logs to help identify security incidents. Finally, you will review detecting and investigating security incidents
with AWS services.
Activities
This course includes presentations, hands-on labs, demonstrations, and group exercises.
Course objectives
In this course, you will learn to:
State an understanding of AWS cloud security based on the CIA triad.
Create and analyze authentication and authorizations with IAM.
Manage and provision accounts on AWS with appropriate AWS services.
Identify how to manage secrets using AWS services.
Monitor sensitive information and protect data via encryption and access controls.
Identify AWS services that address attacks from external sources.
Monitor, generate, and collect logs.
Identify indicators of security incidents.
Identify how to investigate threats and mitigate using AWS services.
Intended audience
This course is intended for:
Security engineers
Security architects
Cloud architects
Cloud operators working across all global segments.
Prerequisites
We recommend that attendees of this course have:
Completed the following courses:
AWS Security Essentials (Classroom training) or
AWS Security Fundamentals (Second Edition) (digital) and
Architecting on AWS (Classroom Training)
Working knowledge of IT security practices and infrastructure concepts.
Familiarity with the AWS Cloud.
Course outline
Day 1
Module 1: Security Overview and Review
Explain Security in the AWS Cloud.
Explain AWS Shared Responsibility Model.
Summarize IAM, Data Protection, and Threat Detection and Response.
State the different ways to interact with AWS using the console, CLI, and SDKs.
Describe how to use MFA for extra protection.
State how to protect the root user account and access keys.
Module 2: Securing Entry Points on AWS
Describe how to use multi-factor authentication (MFA) for extra protection.
Describe how to protect the root user account and access keys.
Describe IAM policies, roles, policy components, and permission boundaries.
Explain how API requests can be logged and viewed using AWS CloudTrail and how to view and analyze access history.
Hands-On Lab: Using Identity and Resource Based Policies.
Module 3: Account Management and Provisioning on AWS
Explain how to manage multiple AWS accounts using AWS Organizations and AWS Control Tower.
Explain how to implement multi-account environments with AWS Control Tower.
Demonstrate the ability to use identity providers and brokers to acquire access to AWS services.
Explain the use of AWS IAM Identity Center (successor to AWS Single Sign-On) and AWS Directory Service.
Demonstrate the ability to manage domain user access with Directory Service and IAM Identity Center.
Hands-On Lab: Managing Domain User Access with AWS Directory Service
Day 2
Module 4: Secrets Management on AWS
Describe and list the features of AWS KMS, CloudHSM, AWS Certificate Manager (ACM), and AWS Secrets Manager.
Demonstrate how to create a multi-Region AWS KMS key.
Demonstrate how to encrypt a Secrets Manager secret with an AWS KMS key.
Demonstrate how to use an encrypted secret to connect to an Amazon Relational Database
Service (Amazon RDS) database in multiple AWS Regions
Hands-on lab: Lab 3: Using AWS KMS to Encrypt Secrets in Secrets Manager
Module 5: Data Security
Monitor data for sensitive information with Amazon Macie.
Describe how to protect data at rest through encryption and access controls.
Identify AWS services used to replicate data for protection.
Determine how to protect data after it has been archived.
Hands-on lab: Lab 4: Data Security in Amazon S3
Module 6: Infrastructure Edge Protection
Describe the AWS features used to build secure infrastructure.
Describe the AWS services used to create resiliency during an attack.
Identify the AWS services used to protect workloads from external threats.
Compare the features of AWS Shield and AWS Shield Advanced.
Explain how centralized deployment for AWS Firewall Manager can enhance security.
Hands-on lab: Lab 5: Using AWS WAF to Mitigate Malicious Traffic
Day 3
Module 7: Monitoring and Collecting Logs on AWS
Identify the value of generating and collecting logs.
Use Amazon Virtual Private Cloud (Amazon VPC) Flow Logs to monitor for security events.
Explain how to monitor for baseline deviations.
Describe Amazon EventBridge events.
Describe Amazon CloudWatch metrics and alarms.
List log analysis options and available techniques.
Identify use cases for using virtual private cloud (VPC) Traffic Mirroring.
Hands-on lab: Lab 6: Monitoring for and Responding to Security Incidents
Module 8: Responding to Threats
Classify incident types in incident response.
Understand incident response workflows.
Discover sources of information for incident response using AWS services.
Understand how to prepare for incidents.
Detect threats using AWS services.
Analyze and respond to security findings.
Hands-on lab: Lab 7: Incident Response
AMA33 - The Machine Learning Pipeline on AWS
Длительность: 4 дня (32 часа)
Код курса: AMA33
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Course description
This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or
flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.
Activities
This course includes presentations, group exercises, demonstrations, and hands-on labs.
Course objectives
In this course, you will:
Select and justify the appropriate ML approach for a given business problem
Use the ML pipeline to solve a specific 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
Enroll today
Visit aws.training to find a class today
Course outline
Day 1
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 2
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 3
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 4
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
AMA34 - Video Streaming Essentials for AWS Media Services
Длительность: 2 дня (16 часов)
Код курса: АМА34
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Course description
In this course, you will learn best practices for designing and using cloud-based video workflows. It covers
important concepts related to video processing and delivery, the variables that can impact migration
decisions, and real-world examples of hybrid and cloud use cases for AWS Media Services. It also introduces
security, machine learning, and analytics concepts to help you consider how AWS Media Services fit into
your overall cloud strategy.
Activities
This course includes presentations, demonstrations, and hands-on labs.
Course objectives
In this course, you will learn to:
Articulate the essential terms and concepts fundamental to making video streaming workflow
decisions, including video metrics, compression, distribution, and protocols.
Describe the four fundamental stages of video streaming workflows: ingest, process, store, and
deliver.
Describe which AWS services can be used in each stage of a video streaming workflow,
including ingest, processing, storage, and delivery.
Analyze video streaming workflow diagrams using AWS services, based on simple to complex
use cases.
Recognize the key variables that influence workflow decisions.
Recognize how AWS services for compliance, storage, and compute interact with AWS Media
Services in video streaming workflows and the functions they perform.
Use the AWS Management Console to build and run simple video streaming workflows for live
and video-on-demand content.
Recognize the automation and data analytics available for Media Services when used with
AWS Machine Learning and explore media-specific use cases for these services.
Explain the importance of security in the AWS Cloud and how it is applied in video streaming
workflows.
Intended audience
This course is intended for individuals who work in or are considering migration to AWS Media Services,
including those in the following roles:
Video Operator/Engineer
Developer
Architect
Project Manager/Engineer
Prerequisites
We strongly recommend learners complete the required prerequisites prior to class, and suggest
completion of the optional prerequisites for an optimal experience.
Required:
Video Streaming Concepts: AWS Media Services
Introduction to AWS Media Services by Use Case
Optional:
AWS Technical Essentials (digital or classroom)
Course outline
Day 1
Module 1: Important Video Concepts
Resolution, bitrate, frame rate, latency, and compression
Codecs and containers
Group of pictures (GOP) encoding
ABR, packaging and distribution
Internet protocols used in video streaming
Module 2: Anatomy of Streaming Workflows
Four stages of video streaming
Variables that affect design decisions
Module 3: Using AWS Services in Video-on-Demand (VOD) Workflows
Converting a film or tape library for internet streaming
Increasing reach and accessibility using multiple languages and captions
Streaming edited highlights from a live event
Analyzing and tagging VOD files for media content analysis using machine learning and
data analytics
Day 2
Module 4: Optimizing Workflows
Security
Migrating to the cloud
Cloud financial management
Module 5: Using AWS Services in Live Workflows
Challenges of live streaming
Live streaming a simple interview show
Live streaming a major sporting event to a global audience
Live switching between multiple inputs
Saving segments from a live show to create VOD segments
Module 6: Recap and Review
Module 7: Next Steps