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. Intended audience: This course is intended for students wanting to extend their knowledge of the data processing options available in AWS.
While there are no formal pre-requisites students will benefit from having a basic understanding of cloud computing services Recommended course - Compute Fundamentals
- 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 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 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.