Amazon Web Services Elastic MapReduce
Amazon Web Services Lambda
The course is part of these learning paths
In this course for the Big Data Specialty Certification, we learn how to identify the appropriate data processing technologies needed for big data scenarios. We explore how to design and architect a data processing solution, and explore and define the operational characteristics of big data processing.
- Recognize and explain how to identify the appropriate data processing technologies needed for big data scenarios.
- Recognize and explain how to design and architect a data processing solution.
This course is intended for students wanting to extend their knowledge of the data processing options available in AWS.
While there are no formal prerequisites for this course, students will benefit from having a basic understanding of cloud computing services. If you would like to gain a solid foundation in compute fundamentals, then check out our Compute Fundamentals For AWS course.
This Course Includes
75 minutes of high-definition video.
What You'll Learn
- Course Intro: What to expect from this course
- Amazon Elastic MapReduce Overview: In this lesson, we discuss how EMR allows you to store and process data
- Amazon Elastic MapReduce Architecture: In this lesson, you’ll learn about EMR’s clustered architecture.
- Amazon Elastic MapReduce in Detail: In this lesson, we’ll dig deeper into EMR storage options, resource management, and processing options.
- Amazon Elastic MapReduce Reference Architecture: Best practices for using EMR.
- Amazon Lambda Introduction: This lesson will kick off our discussion of Lambda and how it’s used in Big Data scenarios.
- Amazon Lambda Overview: This lesson discusses how Lambda allows you to run code for virtually any type of application or backend service with no administration.
- AWS Lambda Architecture: In this lesson, we’ll discuss generic Lambda architecture and Amazon’s serverless service.
- AWS Lambda in Detail: In this lesson, we’ll dig into Events and Service Limits.
- AWS Lambda Reference Architecture: In this lesson, we'll look at a real-life scenario of how lambda can be used.
Amazon Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume. There is no charge when your code is not running. With Lambda you can run code for virtually any type of application or backend service, all with zero administration. Just upload your code and Lambda takes care of everything required to run and scale your code with high availability. You can set up your code to automatically trigger from other AWS services or call directly from any web or mobile application. Amazon Lambda sits between two other services, one that triggers Amazon Lambda to execute code based on an event, and the other that receives the output of that code execution. As a serverless service your Amazon Lambda compute instance never persists.
They are provisioned and destroyed automatically as required. When choosing a big data processing solution from within the available AWS service offerings, it is important to determine whether you need the latency of response from the process to be in seconds, minutes or hours. This will typically drive the decision on which AWS service is the best for that processing pattern or use case.
Amazon Lambda is primary designed to deliver processing orientated around real-time streaming. One of the interesting things we need when we look at the storage patterns is that Amazon Lambda does not store persistent data, unlike many of the other AWS big data services. So, Amazon Lambda needs to be deployed as part of a larger solution. It does, however, allow you to store data temporarily based on your code requirements. Amazon Lambda lets you run code without provisioning or managing servers. You can run code virtually on any type of application or backend service, all with zero administration. Amazon Lambda executes your code only when needed, and scales automatically from a few requests per day to thousands per second.
About the Author
Shane has been emerged in the world of data, analytics and business intelligence for over 20 years, and for the last few years he has been focusing on how Agile processes and cloud computing technologies can be used to accelerate the delivery of data and content to users.
He is an avid user of the AWS cloud platform to help deliver this capability with increased speed and decreased costs. In fact its often hard to shut him up when he is talking about the innovative solutions that AWS can help you to create, or how cool the latest AWS feature is.
Shane hails from the far end of the earth, Wellington New Zealand, a place famous for Hobbits and Kiwifruit. However your more likely to see him partake of a good long black or an even better craft beer.