277 Amazon courses in Cardiff delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: Database architects Database administrators Database developers Data analysts and scientists Overview This course is designed to teach you how to: Discuss the core concepts of data warehousing, and the intersection between data warehousing and big data solutions Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3, to contribute to the data warehousing solution Architect the data warehouse Identify performance issues, optimize queries, and tune the database for better performance Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based 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 other 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 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 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 Data Warehousing on AWS AWS Classroom Training Concurrent write operations Troubleshooting load issues Hands-on lab 5: Loading data with the COPY command 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 feature

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: System administrators and operators who are operating in the AWS Cloud Informational technology workers who want to increase the system operations knowledge. Overview In this course, you will learn to: Recognize the AWS services that support the different phases of Operational Excellence, a WellArchitected Framework pillar. Manage access to AWS resources using AWS Accounts and Organizations and AWS Identity and Access Management (IAM). Maintain an inventory of in-use AWS resources using AWS services such as AWS Systems Manager, AWS CloudTrail, and AWS Config. Develop a resource deployment strategy utilizing metadata tags, Amazon Machine Images, and Control tower to deploy and maintain an AWS cloud environment. Automate resource deployment using AWS services such as AWS CloudFormation and AWS Service Catalog. Use AWS services to manage AWS resources through SysOps lifecycle processes such as deployments and patches. Configure a highly available cloud environment that leverages AWS services such as Amazon Route 53 and Elastic Load Balancing to route traffic for optimal latency and performance. Configure AWS Auto Scaling and Amazon Elastic Compute Cloud auto scaling to scale your cloud environment based on demand. Use Amazon CloudWatch and associated features such as alarms, dashboards, and widgets to monitor your cloud environment. Manage permissions and track activity in your cloud environment using AWS services such as AWS CloudTrail and AWS Config. Deploy your resources to an Amazon Virtual Private Cloud (Amazon VPC), establish necessary connectivity to your Amazon VPC, and protect your resources from disruptions of service. State the purpose, benefits, and appropriate use cases for mountable storage in your AWS cloud environment. Explain the operational characteristics of object storage in the AWS cloud, including Amazon Simple Storage Service (Amazon S3) and Amazon S3 Glacier. Build a comprehensive costing model to help gather, optimize, and predict your cloud costs using services such as AWS Cost Explorer and the AWS Cost & Usage Report. This course teaches systems operators and anyone performing system operations functions how to install, configure, automate, monitor, secure, maintain and troubleshoot the services, networks, and systems on AWS necessary to support business applications. The course also covers specific AWS features, tools, andbest practices related to these functions. Module 1: Introduction to System Operations on AWS Systems operations AWS Well-Architected Framework AWS Well-Architected Tool Module 2a: Access Management Access management Resources, accounts, and AWS Organizations Module 2b: System Discovery Methods to interact with AWS services Introduction to monitoring services Tools for automating resource discovery Inventory with AWS Systems Manager and AWS Config Troubleshooting scenario Hands-On Lab: Auditing AWS Resources with AWS Systems Manager and AWS Config Module 3: Deploying and Updating Resources Systems operations in deployments Tagging strategies Deployment using Amazon Machine Images (AMIs) Deployment using AWS Control Tower Troubleshooting scenario Module 4: Automating Resource Deployment Deployment using AWS CloudFormation Deployment using AWS Service Catalog Troubleshooting scenario Hands-On Lab: Infrastructure as Code Module 5: Manage Resources AWS Systems Manager Troubleshooting scenario Hands-On Lab: Operations as Code Module 6a: Configure Highly Available Systems Distributing traffic with Elastic Load Balancing Amazon Route 53 Module 6b: Automate Scaling Scaling with AWS Auto Scaling Scaling with Spot Instances Managing licenses with AWS License Manager Troubleshooting scenario Module 7: Monitor and Maintaining System Health Monitoring and maintaining healthy workloads Monitoring distributed applications Monitoring AWS infrastructure Monitoring your AWS account Troubleshooting scenario Hands-On Lab: Monitoring Applications and Infrastructure Module 8: Data Security and System Auditing Maintain a strong identity and access foundation Implement detection mechanisms Automate incident remediation Troubleshooting scenario Hands-On Lab: Securing the Environment Module 9: Operate Secure and Resilient Networks Building a secure Amazon Virtual Private Cloud (Amazon VPC) Networking beyond the VPC Troubleshooting scenario Module 10a : Mountable Storage Configuring Amazon Elastic Block Storage (Amazon EBS) Sizing Amazon EBS volumes for performance Using Amazon EBS snapshots Using Amazon Data Lifecycle Manager to manage your AWS resources Creating backup and data recovery plans Configuring shared file system storage Module 10b: Object Storage Deploying Amazon Simple Storage Service (Amazon S3) with Access Logs, Cross-Region Replication, and S3 Intelligent-Tiering Hands-On Lab: Automating with AWS Backup for Archiving and Recovery Module 11: Cost Reporting, Alerts, and Optimization Gain AWS expenditure awareness Use control mechanisms for cost management Optimize your AWS spend and usage Hands-On Lab: Capstone lab for SysOps Additional course details: Nexus Humans Systems Operations on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Systems Operations on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: Solutions architects Developers Cost-optimization leads System administrators Overview In this course, you will learn to: Explain the cost of core AWS services Determine and predict costs associated with current and future cloud workloads Use strategies and best practices to reduce AWS costs Use AWS tools to manage, monitor, alert, and optimize your AWS spend Apply strategies to monitor service costs and usage Implement governance standards, including resource tagging, account structure, provisioning,permissions, and access This course is for individuals who seek an understanding of how to manage, optimize, and predict costs as you run workloads on AWS. You learn how to implement architectural best practices, explore cost optimization strategies, and design patterns to help you architect cost-efficient solutions on AWS. Module 0: Couse Overview Course introduction Module 1: Introduction to Cloud Financial Management Introduction to Cloud Financial Management Four pillars of Cloud Financial Management Module 2: Resource Tagging Tagging resources Hands-On Lab: Cost optimization: Control Resource Consumption Using Tagging and AWS Config Module 3: Pricing and Cost Fundamentals of pricing AWS Free Tier Volume discounts Savings plans and Reserved Instances Demonstration: AWS Pricing Calculator Module 4: AWS Billing, Reporting, and Monitoring Understanding AWS invoices Reporting and planning AWS Cost Explorer AWS Budgets Demonstration: AWS Billing Console Demonstration: AWS Cost Explorer Demonstration: Trusted Advisor Hands-On Lab: Cost optimization: Deploy Ephemeral Environments Using Amazon EC2 Auto Scaling Module 5: Architecting for Cost: Compute Evolution of compute efficiency Amazon EC2 right-sizing Purchasing options Architect for Amazon EC2 Spot Instance Impact of software licensing Demonstration: Compute Optimizer Demonstration: Spot Instance Advisor Hands-On Lab: Cost optimization: Right Size Amazon EC2 Instances Using Amazon CloudWatch Metrics Module 6: Architecting for Cost: Networking Data transfer costs Understand data costs for different services How to triage network costs Hands-On Lab: Cost optimization: Reduce Data Transfer Costs Using Amazon CloudFront and Endpoints Module 7: Architecting for Cost: Storage Amazon EBS cost, pricing, and best practices Amazon S3 cost, pricing, and best practices Amazon EFS cost, pricing, and best practices Hands-On Lab: Cost optimization: Reduce Storage Costs Using Amazon S3 Lifecycle Management Module 8: Architecting for Cost: Databases Amazon RDS cost, pricing, and best practices Amazon Aurora cost, pricing, and best practices Amazon DynamoDB cost, pricing, and best practices Amazon ElastiCache cost, pricing, and best practices Amazon Redshift cost, pricing, and best practices Module 9: Cost Governance Setting up AWS Organizations AWS Systems Manager Hands-On Lab: Cost optimization: Reduce Compute Costs Using AWS Instance Scheduler Module 10: Course Summary Course review

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview 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 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. 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

Duration 3 Days 18 CPD hours This course is intended for Data platform engineers Database administrators Solutions architects IT professionals Overview Apply database concepts, database management, and data modeling techniques Evaluate hosting databases on Amazon EC2 instances Evaluate relational database services (Amazon RDS, Amazon Aurora, and Amazon Redshift) and their features Evaluate nonrelational database services (Amazon DocumentDB, Amazon DynamoDB, Amazon ElastiCache, Amazon Neptune, and Amazon QLDB) and their features Examine how the design criteria apply to each service Apply management principles based on the unique features of each service This course will teach you the process of planning and designing both relational and nonrelational database and the planning and design requirements of all 8 of the AWS databases services, their pros and cons, and how to know which AWS databases service is right for your workloads. Day 1 Module 0: Planning and Designing Databases on AWS Module 1: Database Concepts and General Guidelines Module 2: Database Planning and Design Module 3: Databases on Amazon EC2 Module 4: Purpose-Built Databases Module 5: Databases on Amazon RDS Databases in Amazon Aurora Day 2 Module 6: Databases in Amazon Aurora (continued) Module 7: Databases in Amazon DocumentDB (with MongoDB compatibility) Module 8: Amazon DynamoDB Tables Day 3 Module 9: Databases in Amazon Neptune Module 10: Databases in Amazon Quantum Ledger Database (Amazon QLDB) Module 11: Databases in Amazon ElastiCache Module 12: Data Warehousing in Amazon Redshift Module 13: Course Review Additional course details: Nexus Humans Planning and Designing Databases on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Planning and Designing Databases on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for Developers who have some familiarity with serverless and experience with development in the AWS Cloud Overview 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 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. Introduction Introduction to the application you will build Access to course resources (Student Guide, Lab Guide, and Online Course Supplement) Thinking Serverless Best practices for building modern serverless applications Event-driven design AWS services that support event-driven serverless applications 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) 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 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 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 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 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 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 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 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 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 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 Additional course details: Nexus Humans AWS Developing Serverless Solutions on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AWS Developing Serverless Solutions on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: DevOps engineers DevOps architects Operations engineers System administrators Developers Overview In this course, you will learn to: 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 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. 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 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 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

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers System Administrators Solutions Architects Overview This course is designed to teach you how to: Design a microservices-based architecture that uses containers Use Amazon ECS to run and scale a microservices-based application Integrate Amazon ECS with other AWS services Running Container-Enabled Microservices on AWS is designed to teach you how to manage and scale container-enabled applications by using Amazon Elastic Container Service (ECS). This course highlights the challenges of running containerized applications at scale and provides guidance on creating and using Amazon ECS to develop and deploy containerized microservices-based applications. In the hands-on lab exercises you will use Amazon ECS to handle long-running services, build and deploy container images, link services together, and scale capacity to meet demand. You will also learn how to run container workers for asynchronous application processes. Module 1a: Overview of Microservices on AWS Welcome to Simple Mustache Service! The monolith What are microservices? How to implement a microservices infrastructure The six principles of microservices Module 1b: Containers and Docker Introduction to containers Comparing virtual machines with containers Docker Running containers Storing container images Hands-on lab: Building and running your first container Module 2: Continuous delivery for container-based microservices Compare and contrast different software development cycles Use AWS CodePipeline to code, build, and deploy a microservice Use AWS CodeCommit as a source control service Use Jenkins to perform a Docker build Use Postman to run and test microservices Use AWS CloudFormation to provision and deploy microservices Hands-on lab: Using the Amazon ECS Service Scheduler Module 3: High availability and scaling with Amazon Elastic Container Service High availability Cluster management and scheduling Monitoring Scaling a cluster Scaling services Hands-on lab: Continuous delivery pipelines for container-based microservices Module 4: Security for container-based microservices Implement security Apply best practices Automate security Evaluate compliance requirements Embed security into the CI/CD Hands-on lab: Extending Amazon ECS with Service Discovery and Config Management Additional course details: Nexus Humans Running Container Enabled Microservices on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Running Container Enabled Microservices on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Individuals planning to deploy applications and create application environments on Google Cloud Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview This course teaches participants the following skills: Identify Google Cloud counterparts for AWS IaaS, AWS PaaS, AWS SQL, AWS Blob Storage, AWS Application Insights, and AWS Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences This course with labs introduces AWS professionals to the core capabilities of Google Cloud Platform (GCP) in the four technology pillars: networking, compute, storage, and database. It is designed for AWS Solution Architects and SysOps Administrators familiar with AWS features and setup and want to gain experience configuring GCP products immediately. With presentations, demos, and hands-on labs, participants will get details of similarities, differences, and initial how-tos quickly. Introducing Google Cloud Explain the advantages of Google Cloud. Define the components of Google's network infrastructure,including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Getting Started with Google Cloud Identify the purpose of projects on Google Cloud Platform. Understand how AWS?s resource hierarchy differs from Google Cloud?s. Understand the purpose of and use cases for Identity and Access Management. Understand how AWS IAM differs from Google Cloud IAM. List the methods of interacting with Google Cloud Platform. Launch a solution using Cloud Marketplace. Virtual Machines in the Cloud Identify the purpose and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud Platform. Understand how Amazon VPC differs from Google VPC. Understand the similarities and differences between Amazon EC2 and Google Compute Engine. Understand how typical approaches to load-balancing in Google Cloud differ from those in AWS. Deploy applications using Google Compute Engine. Storage in the Cloud Understand the purpose of and use cases for: Cloud Storage,Cloud SQL, Cloud Bigtable and Cloud Datastore. Understand how Amazon S3 and Amazon Glacier compare to Cloud Storage. Compare Google Cloud?s managed database services with Amazon RDS and Amazon Aurora. Learn how to choose among the various storage options on Google Cloud Platform. Load data from Cloud Storage into BigQuery. Perform a query on the data in BigQuery. Containers in the Cloud Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Understand how Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) differ from GKE. Provision a Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl Applications in the Cloud Understand the purpose of and use cases for Google App Engine. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand how App Engine differs from Amazon Elastic Beanstalk. Understand the purpose of and use cases for Google Cloud Endpoints. Developing, Deploying and Monitoring in the Cloud Understand options for software developers to host their source code. Understand the purpose of template-based creation and management of resources. Understand how Cloud Deployment Manager differs from AWS CloudFormation. Understand the purpose of integrated monitoring, alerting, and debugging. Understand how Google Monitoring differs from Amazon CloudWatch and AWS CloudTrail. Create a Deployment Manager deployment. Update a Deployment Manager deployment. View the load on a VM instance using Google Monitoring. Big Data and Machine Learning in the Cloud Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Understand how Google Cloud BigQuery differs from AWS Data Lake. Understand how Google Cloud Pub/Sub differs from AWS Event Hubs and Service Bus. Understand how Google Cloud?s machine-learning APIs differ from AWS's. Load data into BigQuery from Cloud Storage. Perform queries using BigQuery to gain insight into data. Summary and Review Review the products that make up Google Cloud and remember how to choose among them Understand next steps for training and certification Understand, at a high level, the process of migrating from AWS to Google Cloud. Additional course details: Nexus Humans Google Cloud Fundamentals for AWS Professionals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Google Cloud Fundamentals for AWS Professionals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for 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 Overview In this course, you will learn to: 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 This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the 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. 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 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 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 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 Additional course details: Nexus Humans The Machine Learning Pipeline on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the The Machine Learning Pipeline on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.