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Duration 3 Days 18 CPD hours This course is intended for This course is intended for solutions architects, solution-design engineers, developers seeking an understanding of AWS architecting and individuals seeking the AWS Solutions Architect-Associate certification. Overview Identify AWS architecting basic practices. Explore using the AWS management tools: The AWS Console, Command Line Interface (CLI), and CloudFormation in a lab environment. Examine the enforcement of accounts security using policies. Identify the elements that build an elastic, secure, virtual network that includes private and public subnets. Practice building an AWS core networking infrastructure. Determine strategies for a layered security approach to Virtual Private Cloud (VPC) subnets. Identify strategies to select the appropriate compute resources based on business use-cases. Practice building a VPC and adding an Elastic Cloud Compute (EC2) instance in a lab environment. Practice installing an Amazon Relational Database Service (RDS) instance and an Application Load Balancer (ALB) in the VPC you created. Compare and contrast AWS storage products and services, based on business scenarios. Compare and contrast the different types of AWS database services based on business needs. Practice building a highly available, auto-scaling database layer in a lab. Explore the business value of AWS monitoring solutions. Identify the role of monitoring, event driven load balancing, and auto scaling responses, based on usage and needs. Identify and discuss AWS automation tools that will help you build, maintain and evolve your infrastructure. Discuss network peering, VPC endpoints, gateway and routing solutions based on use-cases. Discuss hybrid networking configurations to extend and secure your infrastructure. Discuss the benefits of microservices as an effective decoupling strategy to power highly available applications at scale. 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. Practice building a serverless infrastructure in a lab environment. Discuss the ways in which AWS edge services address latency and security. Practice building a CloudFront deployment with an S3 backend in a lab environment. Explore AWS backup, recovery solutions, and best practices to ensure resiliency and business continuity. Build a highly available and secure cloud architecture based on a business problem, in a project-based facilitator-guided lab. 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 with confidence. Prerequisites AWS Cloud Practitioner Essentials classroom or digital training, or 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 0 - Introductions & Course Map review Welcome and course outcomes 1 - Architecting Fundamentals Review AWS Services and Infrastructure Infrastructure Models AWS API Tools Securing your infrastructure The Well-Architected Framework Hands-on lab: Explore Using the AWS API Tools to Deploy an EC2 Instance 2 - Account Security Security Principals Identity and Resource-Based Policies Account Federation Introduction to Managing Multiple Accounts 3 - Networking, Part 1 IP Addressing Amazon Virtual Private Cloud (VPC), Patterns and Quotas Routing Internet Access Network Access Control Lists (NACLs) Security Groups 4 - Compute Amazon Elastic Cloud Compute (EC2) EC2 Instances and Instance Selection High Performance Computing on AWS Lambda and EC2, When to Use Which Hands-On Lab: Build Your Amazon VPC Infrastructure 5 - Storage Amazon S3, Security, Versioning and Storage Classes Shared File Systems Data Migration Tools 6 - Database Services AWS Database Solutions Amazon Relational Database Services (RDS) DynamoDB, Features and Use Cases Redshift, Features, Use Cases and Comparison with RDS Caching and Migrating Data Hands-on Lab: Create a Database Layer in Your Amazon VPC Infrastructure 7 - Monitoring and Scaling Monitoring: CloudWatch, CloudTrail, and VPC Flow Logs Invoking Events 8 - Automation CloudFormation AWS Systems Manager 9 - Containers Microservices Monitoring Microservices with X-Ray Containers 10 - Networking Part 2 VPC Peering & Endpoints Transit Gateway Hybrid Networking Route 53 11 - Serverless Architecture Amazon API Gateway Amazon SQS, Amazon SNS Amazon Kinesis Data Streams & Kinesis Firehose Step Functions Hands-on Lab: Build a Serverless Architecture 12 - Edge Services Edge Fundamentals Amazon CloudFront AWS Global Accelerator AWS Web Application Firewall (WAF), DDoS and Firewall Manager AWS Outposts Hands-On Lab: Configure an Amazon CloudFront Distribution with an Amazon S3 Origin 13 - Backup and Recovery Planning for Disaster Recovery AWS Backup Recovery Strategie Additional course details: Nexus Humans Architecting 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 Architecting 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 Solution Architects Overview At the end of this course, you will be able to: Apply the AWS Well-Architected Framework Manage multiple AWS accounts for your organization Connect an on-premises datacenter to AWS cloud Move large data from an on-premises datacenter to AWS Design large datastores for AWS cloud Understand different architectural designs for scalability Protect your infrastructure from DDoS attack Secure your data on AWS with encryption Enhance the performance of your solutions Select the most appropriate AWS deployment mechanism Building on concepts introduced in Architecting on AWS, Advanced Architecting on AWS is intended for individuals who are experienced with designing scalable and elastic applications on the AWS platform. Building on concepts introduced in Architecting on AWS, this course covers how to build complex solutions which incorporate data services, governance, and security on AWS. This course introduces specialized AWS services, including AWS Direct Connect and AWS Storage Gateway to support Hybrid architecture. It also covers designing best practices for building scalable, elastic, secure, and highly available applications on AWS. Module 1: AWS Account Management Multiple accounts Multi-account patterns License management Manage security and costs with multiple accounts AWS Organizations AWS Directory Service Hands-on lab: Multi-VPC connectivity using a VPN Module 2: Advanced Network Architectures Improve VPC network connections Enhance performance for HPC workloads VPN connections over AWS AWS Direct Connect AWS Transit Gateway Amazon Route 53 Exercise: Design a hybrid architecture Module 3: Deployment Management on AWS Application lifecycle management Application deployment using containers AWS Elastic Beanstalk AWS OpsWorks AWS CloudFormation Module 4: Data Optimize Amazon S3 storage Amazon ElastiCache AWS Snowball AWS Storage Gateway AWS DataSync Backup and archival considerations Database migration Designing for big data with Amazon DynamoDB Hands-on lab: Build a failover solution with Amazon Route 53 and Amazon RDS Module 5: Designing for large scale applications AWS Auto Scaling Migrating over-provisioned resources Blue-green deployments on AWS Hands-on lab: Blue-green deployment with AWS Module 6: Building resilient architectures DDoS attack overview AWS Shield AWS WAF Amazon GuardDuty High availability using Microsoft SQL Server and Microsoft SharePoint on AWS High availability using MongoDB on Amazon EC2 AWS Global Accelerator Hands-on lab: CloudFront content delivery and automating AWS WAF rules Module 7: Encryption and data security Encryption primer DIY key management in AWS AWS Marketplace for encryption products AWS Key Management Service (AWS KMS) Cloud Hardware Security Module (HSM) Comparison of key management options Hands-on lab: AWS KMS with envelope encryption

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: A technical audience at an intermediate level Overview Using Amazon SageMaker, this course teaches you how to: Prepare a dataset for training. Train and evaluate a machine learning model. Automatically tune a machine learning model. Prepare a machine learning model for production. Think critically about machine learning model results In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. Day 1 Business problem: Churn prediction Load and display the dataset Assess features and determine which Amazon SageMaker algorithm to use Use Amazon Sagemaker to train, evaluate, and automatically tune the model Deploy the model Assess relative cost of errors Additional course details: Nexus Humans Practical Data Science with Amazon SageMaker 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 Practical Data Science with Amazon SageMaker 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: Intermediate software developers Overview In this course, you will learn to: Set up the AWS SDK and developer credentials for Java, C#/.NET, Python, and JavaScript Interact with AWS services and develop solutions by using the AWS SDK Use AWS Identity and Access Management (IAM) for service authentication Use Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB as data stores Integrate applications and data by using AWS Lambda, Amazon API Gateway, Amazon Simple Queue Service (Amazon SQS), Amazon Simple Notification Service (Amazon SNS), and AWS Step Functions Use Amazon Cognito for user authentication Use Amazon ElastiCache to improve application scalability Leverage the CI/CD pipeline to deploy applications on AWS In this course, you learn how to use the AWS SDK to develop secure and scalable cloud applications using multiple AWS services such as Amazon DynamoDB, Amazon Simple Storage Service, and AWS Lambda. You explore how to interact with AWS using code and learn about key concepts, best practices, and troubleshooting tips. Module 0: Course Overview Agenda Introductions Student resources Module 1: Introduction to AWS Introduction to the AWS Cloud Cloud scenarios Infrastructure overview Introduction to AWS foundation services Module 2: Introduction to Developing on AWS Getting started with developing on AWS Introduction to developer tools Introduction to management tools Module 3: Introduction to AWS Identity and Access Management Shared responsibility model Introduction to IAM Use authentication and authorization Module 4: Introduction to the Lab Environment Introduction to the lab environment Lab 1: Getting started and working with IAM Module 5: Developing Storage Solutions with Amazon Simple Storage Service Overview of AWS storage options Amazon S3 key concepts Best practices Troubleshooting Scenario: Building a complete application Lab 2: Developing storage solutions with Amazon S3 Module 6: Developing Flexible NoSQL Solutions with Amazon DynamoDB Introduction to AWS database options Introduction to Amazon DynamoDB Developing with DynamoDB Best practices Troubleshooting Scenario: Building an end-to-end app Lab 3: Developing flexible NoSQL solutions with Amazon DynamoDB Module 7: Developing Event-Driven Solutions with AWS Lambda What is serverless computing? Introduction to AWS Lambda Key concepts How Lambda works Use cases Best practices Scenario: Build an end-to-end app Module 8: Developing Solutions with Amazon API Gateway Introduction to Amazon API Gateway Developing with API Gateway Best practices Introduction to AWS Serverless Application Model Scenario: Building an end-to-end app Lab 4: Developing event-driven solutions with AWS Lambda Module 9: Developing Solutions with AWS Step Functions Understanding the need for Step Functions Introduction to AWS Step Functions Use cases Module 10: Developing Solutions with Amazon Simple Queue Service and Amazon Simple Notification Service Why use a queueing service? Developing with Amazon Simple Queue Service Developing with Amazon Simple Notification Service Developing with Amazon MQ Lab 5: Developing messaging solutions with Amazon SQS and Amazon SNS Module 11: Caching Information with Amazon ElastiCache Caching overview Caching with Amazon ElastiCache Caching strategies Module 12: Developing Secure Applications Securing your applications Authenticating your applications to AWS Authenticating your customers Scenario: Building an end-to-end app Module 13: Deploying Applications Introduction to DevOps Introduction to deployment and testing strategies Deploying applications with AWS Elastic Beanstalk Scenario: Building an end-to-end app Lab 6: Building an end-to-end app Module 14: Course wrap-up Course overview AWS training courses Certifications Course feedback

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.