Kinesis Feature Comparisons

The course is part of these learning paths

DevOps Engineer – Professional Certification Preparation for AWS
course-steps 35 certification 5 lab-steps 18 quiz-steps 2 description 3
Solutions Architect – Professional Certification Preparation for AWS
course-steps 47 certification 6 lab-steps 19 quiz-steps 4 description 2
Certified Developer – Associate Certification Preparation for AWS
course-steps 29 certification 5 lab-steps 22 description 2
AWS Big Data – Specialty Certification Preparation for AWS
course-steps 14 certification 1 lab-steps 4 quiz-steps 4
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Contents

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Course Introduction
1
Introduction
PREVIEW41s
Kinesis Analytics
Course Conclusion
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Start course
Overview
DifficultyIntermediate
Duration25m
Students1490
Ratings
3.7/5
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Description

Course Description:

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 the 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.

Updates:

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

Transcript

Welcome back to Working with Amazon Kinesis. I'm Richard Augenti, and I'll be your instructor for this lesson. In this lesson, we'll compare some products within the Amazon Kinesis suite, as well as some other Amazon services. Between Amazon Kinesis Streams and Firehose, Firehose can automatically add and remove the resources to match the current workload. Amazon Streams needs to have some necessary logic with the API built into your application to manage the resources.

Out of the box, you'll need to plan on the shards needed to support your Amazon Streams workload. Amazon Kinesis Streams and Simple Queue services are somewhat similar in their workload. Again, Kinesis Streams doesn't scale out of the box without customizing your application with the API, whereas Amazon Simple Queue Services, resources are fully managed to scale up and down. On the downside, Simple Queue Services can only handle a fraction of the workload that Amazon Kinesis Streams can. Well, that's it for our lesson. I'm Richard Augenti and I'll see you in the next lesson.

About the Author

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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.