This lab introduces you to AWS CloudFront, a content delivery web service. In this lab you will create an Amazon CloudFront distribution that will use a CloudFront domain name in the url to distribute a publicly accessible image file stored in an Amazon S3 bucket. For a demonstration, go to: http://youtu.be/dV5qOxwAJlU For the lab to function as written, please DO NOT change the auto assigned region.
Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. This hand-son lab will demonstrate how Amazon Kinesis Firehose can capture and automatically load streaming data into an Elasticsearch cluster.
This lab provides a basic understanding and hands-on experience of AWS Key Management Service. It will demonstrate the basic steps required to get started with Key Management Service, creating keys, assigning management and usage permissions for the keys, encrypting data and monitoring the access and usage of keys. For the lab to function as written, please DO NOT change the auto assigned region.
This lab takes you through how to create an Amazon Elastic File System (EFS) file system, mount it to an Amazon EC2 instance, run a simple IO benchmark and examine performance characteristics in Amazon CloudWatch. For the lab to function as written, please DO NOT change the auto assigned region.
In this lab, you will run a simple IoT device simulator on Amazon EC2. The device simulator will generate and publish sample sensor data to an AWS device gateway. You will then build a simple rule that will publish a notification to an AWS SNS topic when the temperature of the device is within a defined threshold. By connecting your email address with the SNS topic, you will receive an email notification when the threshold is met. Finally, you will update the device shadow, instructing the device to “turn on the air conditioning”, resulting in lowering temperatures.
In this lab, you will deploy a fully functional Hadoop cluster, ready to analyze log data in just a few minutes. You will start by launching an Amazon EMR cluster and then use a HiveQL script to process sample log data stored in an Amazon S3 bucket. HiveQL is a SQL-like scripting language for data warehousing and analysis. You can then use a similar setup to analyze your own log files.
The lab will give you the basic understanding of Amazon Elasticsearch Service (ES). It will demonstrate the basic steps required to get started with Amazon ES: creating clusters, cluster node configurations, storage configurations and Identity & Access Management (IAM) Policies. As prerequisites you should have already taken the “Introduction to Amazon Elastic Compute Cloud (EC2)” and “Introduction to AWS Identity and Access Management (IAM)” labs. Previous knowledge of Kibana4 and Elasticsearch is desirable.