In this technical demonstration, you will learn how Amazon CloudWatch Events is used and how it integrates with Amazon EventBridge. You will first review a CloudWatch Event rule that will send a message to an Amazon Simple Queue Service (Amazon SQS) queue. Next, you will see how to set up an EventBridge bus to integrate with a partner software-as-a-service (SaaS) application. Finally, you will view demonstrations, including a scenario with a sample producer or consumer application that uses EventBridge. The example scenarios will show integration of other Amazon Web Services (AWS) offerings, such as Amazon API Gateway, AWS Lambda, and Amazon CloudWatch Logs.

In this course, you will learn the benefits and technical concepts of Amazon EventBridge. You will learn about building event-driven architectures and automating workflows to streamline operations and enhance application responsiveness. If you are new to the service, you will learn how to start using Amazon EventBridge through a demonstration that uses the AWS Management Console.

With Amazon File Cache, you can create a temporary, high-speed storage location for your file data in Amazon Web Services (AWS). In this course, you will learn the benefits and technical concepts of Amazon File Cache. If you are new to the service, you will learn how to start using Amazon File Cache through a demonstration using the AWS Management Console. You will learn about how Amazon File Cache brings your data closer to where it's being processed, regardless of where that data originally resides.

Amazon Fraud Detector is a fully managed machine learning service that identifies potentially fraudulent activities in real-time. The service analyzes your data to detect fraud patterns and provides predictions with confidence scores to help you make informed decisions about transactions, account registrations, and other business activities. Learn how to effectively use Amazon Fraud Detector to build scalable and reliable cloud solutions.

Amazon FSx for NetApp ONTAP provides fully managed shared storage in the Amazon Web Services (AWS) Cloud. In includes popular features, data access options, and management capabilities of NetApp ONTAP.

In this course you will learn about the features, benefits, and common use cases for FSx for ONTAP. You will learn how to deploy FSx for ONTAP file systems. We conclude with an examination of some of the post-deployment FSx for ONTAP considerations, such as the pricing, management, and monitoring options available to you.

This course teaches you how Amazon FSx for Windows File Server can support your Windows-based applications and workloads. In this course, you learn about the benefits and use cases of FSx for Windows File Server. You explore the basics of configuring a file system. You also learn about the best practices to follow when administering your file systems. Finally, you review the cost model associated with using Amazon FSx for Windows File Server.

• Course level: Intermediate    
• Duration: 1 hour

With Amazon FSx, you can deploy fully managed file systems in the AWS Cloud without managing the underlying infrastructure. In this course, you will learn the benefits and technical concepts of Amazon FSx. If you are new to the service, you will learn how to start using Amazon FSx through a demonstration using the AWS Management Console. You will learn about how Amazon FSx eliminates the complexity of managing file storage while providing enterprise-grade features and performance.

Amazon GuardDuty is a threat detection service that continuously monitors your AWS accounts, workloads, and data for malicious activity. With GuardDuty, you can use machine learning, anomaly detection, and integrated threat intelligence to identify unexpected and potentially unauthorized activity within your AWS environment. Learn how to use Amazon GuardDuty effectively to build scalable and reliable cloud solutions.

With Amazon Kinesis Data Streams, you can build applications that process streaming data in real-time. Kinesis Data Streams offers a scalable and durable solution for ingesting, processing, and storing streaming data from multiple sources, making it available for use cases such as analytics, monitoring, and machine learning. This course is designed to provide learners with a comprehensive introduction to Amazon Kinesis Data Streams, a fully managed, streaming data service. You'll learn the basics of Amazon Kinesis, its architecture, and its various use cases. The course will guide you through the process of launching and creating a Kinesis stream, sending data to the stream, and consuming data from it.

Amazon Lex is a fully managed artificial intelligence (AI) service with advanced natural language models to design, build, test, and deploy conversational interfaces for voice and text.

In this course, you will learn the benefits and technical concepts of Lightsail. If you are new to the service, you will learn how to start using Lightsail through a demonstration using the AWS Management Console. You will learn about a service that helps you get started with Amazon Web Services (AWS) without dealing with complex infrastructure management.

⛔️ This has been decommissioned by AWS.

Alternatively, you can use the related course from the AWS Skill Builder site by going to this link: AWS Skill Builder.

In this course, you will learn the benefits and technical concepts of Amazon Managed Service for Apache Flink. If you are new to the service, you will learn how to start using Amazon Managed Service for Apache Flink through a demonstration using the AWS Management Console. You will learn about how to process real-time streaming data efficiently without the operational overhead of managing Flink clusters.

With Amazon Managed Streaming for Apache Kafka (Amazon MSK), you can build and run applications that use Apache Kafka to process real-time data streams. In this course, you will learn the benefits and technical concepts of Amazon MSK. If you are new to the service, you will learn how to start using Amazon MSK through a demonstration using the AWS Management Console. You will learn about using a fully managed service to simplify the process of building and running Apache Kafka clusters, eliminating the need to manage infrastructure.

With Amazon Managed Workflows for Apache Airflow (Amazon MWAA), you can build, schedule, and monitor complex data pipelines and workflows without managing the underlying infrastructure. In this course, you will learn the benefits and technical concepts of Amazon MWAA. If you are new to the service, you will learn how to start using Amazon MWAA through a demonstration using the AWS Management Console. You will learn how Amazon MWAA can streamline data processing tasks, automate repetitive operations, and orchestrate multi-step workflows.

With Amazon MQ, you can create message queues and topics, which organize and distribute messages differently. In this course, you will learn the benefits and technical concepts of Amazon MQ. If you are new to the service, you will learn how to start using Amazon MQ through a demonstration using the AWS Management Console. You will learn about how Amazon MQ enables applications and services to communicate and exchange data, even when running on different systems or platforms.

With Amazon Polly, you can convert written text into natural human-like speech allowing applications to speak with natural-sounding voices.

Amazon Q Business is generative artificial intelligence (generative AI) powered assistant that can answer questions, generate content, create summaries, and complete tasks—all based on the information in your enterprise.

Amazon Q Developer is a generative artificial intelligence (AI) powered assistant that helps you understand, build, extend, and operate AWS applications throughout the software development lifecycle. In this Getting Started course, you will learn about the benefits, features, typical use cases, technical concepts, and cost of using Amazon Q Developer. You will review an architecture that uses Amazon Q Developer in interactive and asynchronous environments to chat with applications and customize and transform code. Through a guided tutorial consisting of a narrated video, step-by-step instructions and transcript, you will learn how to setup an interactive development environment (IDE) that uses Amazon Q Developer. You will use that environment to optimize code, transform code written in Java 8 to Java 17, and implement a new feature in a project.