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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.
- People working with Big Data
- Business intelligence
- 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.
28/05/2019 - Re-record of lectures to improve audio
elcome back to Working with Amazon Kinesis. I'm Richard Augenti and I'll be your instructor for this lesson. In this lesson we're gonna wrap up the course with a quick review and some main takeaways. Amazon Kinesis Stream is mainly used for streaming solutions which it'll filter own applications to load, transform and deliver your data. It's highly scalable and flexible and has a heavier focus on using the Kinesis Stream's APIs and libraries. Amazon Kinesis Firehose is similar to the function as Amazon Kinesis Streams.
The main difference is that it's a fully managed service which handles all the administration of the workload for you. Amazon Kinesis Analytics is a fully manged analytics solution. It provides an easy way to process and analyze streaming data with standard and SQL. The console provides a simple way to work with the streaming data by providing tools, to edit the schema low and building queries. I recommend the entire in Amazon Kinesis documentation to get more acquainted with the overall product.
I also recommend working through all tutorials since it's best learned by doing. Well, that wraps up our review in our course. It's been a pleasure spending this time with you on this course and I wish you well with your continued journey with Amazon Kinesis and streaming data. I'm Richard Augenti and I thank you for joining me with Working with Amazon Kinesis.
About the Author
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.