Start course

This course will provide you with a good foundation to better understand Amazon Kinesis, along with helping you to get started with building streamed solutions. In this course, we'll put a heavier emphasis on hands-on demos along with breaking down the concepts and introducing you to the components that make up Amazon Kinesis.

Intended audience

  • People working with Big Data
  • Business intelligence
  • DevOps
  • Development

Learning Objectives

  • Demonstrate knowledge of Amazon Kinesis, what are the core aspects of the service (streams, firehose, analytics), their common use cases, big data use cases, and how do you access / integrate with the Kinesis service Demonstrate how to work with and create Kinesis streams
  • Demonstrate how to work with Kinesis Firehose and how to use Firehose with Redshift
  • Set up monitoring and analysis of the stream services
  • Understand how to extract analysis from Kinesis

This Course Includes

  • 45 minutes of high-definition video
  • Live demonstrations on key course concepts

What You'll Learn

  • What is Streaming Data: An overview of streaming data and it’s common uses.
  • Setting Up Kinesis Agent: In this demo, we're working with installing the Amazon Kinesis Stream agent.
  • Kinesis Streams: An overview of Kinesis Streams, what they do, and common use cases.
  • Performing Basic Stream Operations: In this demo, we'll be pulling a basic Amazon Kinesis stream from the command line.
  • Firehose: In this lesson, we'll be discussing the fully managed solution, Amazon Kinesis Firehose.
  • Firehose Delivery Stream: In this demo, we're going to set up an Amazon Kinesis Firehose stream.
  • Testing Delivery Stream: In this lesson, we're going to do a quick follow up to the Firehose stream, and test the data delivery.
  • Kinesis Analytics: In this lesson, we'll go over the analytics components of Kinesis.
  • Kinesis Analytics Demo: In this demo, we're going to begin working with Amazon Kinesis Analytics.
  • Kinesis Features Comparison: In this lesson, we'll compare some products within the Amazon Kinesis suite, as well as some other Amazon services.
  • Course Conclusion: A wrap-up and review of the course.


28/05/2019 - Re-record of lectures to improve audio 


Welcome back to Working with Amazon Kinesis, I'm Richard Augenti, and I'll be your instructor for this demo. In this demo we're going to begin working with Amazon Kinesis Analytics. So from the console we're going to go over to Kinesis Analytics, and we're going to create an application, and we're gonna provide a name called Kinesis Analytics. We're gonna skip over the description since this is a test instance.

So select Create Application and connect to source. So we're gonna provide some dummy data by selecting the create a demo stream. This will take about 30 to 40 seconds to generate our stream, and then build out our schema for application. Okay, great. So, everything has been successful. Our schema has been created and there's the data from the stream. So we'll save and continue.

From here, Go to SQL editor, and we're going to start application. So at this point, we're going to select the template. So, Analytics comes with a bunch of pre-filled templates that you actually can customize to your needs. We're going to select continuous filter, and we're going to apply this filter to the stream. So the data will be loaded into the application table that we talked about in our lecture. So it's ingesting the data and loading into this template. Great, we're going to begin populating the stream.

Okay, so everything looks good here. Our filter's been applied. So let's exit. So currently our solution is actually running. It's collecting data, it's applying a filter, and everything is running as it should. So if we go back over, we actually could refresh it, and load more data to see how it's working. So there we go. We have an update of the data, our filter is working and our applications working 100%. Well, that wraps up our demo on Amazon Kinesis Analytics. I'm Richard Augenti, and I'll see you in the next lesson.

About the Author
Learning Paths

Richard Augenti is a DevOps Engineer with 23 years of IT professional experience and 7 years of cloud experience with AWS and Azure. He has been engaged with varying sized projects with clients all across the globe including most sectors. He enjoys finding the best and most efficient way to make things work so, working with automation, cloud technologies, and DevOps has been the perfect fit. When Richard is not engaged with work, he can also be found presenting workshops and talks at user conferences on cloud technologies and other techie talks.