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Introduction to EMR

Contents

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Course Introduction
1
Introduction
PREVIEW2m 26s
Utilizing Managed Services and Serverless Architectures to Minimize Cost
Decoupled Architecture
Amazon API Gateway
10
Advanced API Gateway
PREVIEW11m 29s
Amazon Elastic Map Reduce
18
Introduction to EMR
PREVIEW1m 46s
Amazon EventBridge
26
EventBridge
PREVIEW7m 58s
Design considerations
39

The course is part of this learning path

Introduction to EMR
Difficulty
Intermediate
Duration
4h 43m
Students
80
Ratings
3/5
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Description

This section of the AWS Certified Solutions Architect - Professional learning path introduces common AWS solution architectures relevant to the AWS Certified Solutions Architect - Professional exam and the services that support them. These services form a core component of running resilient and performant architectures. 

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Learning Objectives

  • Learn how to utilize managed services and serverless architectures to minimize cost
  • Understand how to use AWS services to process streaming data
  • Discover AWS services that support mobile app development
  • Understand when to utilize serverless services within your AWS solutions
  • Learn which AWS services to use when building a decoupled architecture
Transcript

Hello and welcome to this lecture covering Elastic MapReduce, known as EMR.

Amazon Elastic MapReduce is a managed service designed to process and analyze vast amounts of data through the use of jobs that can be short running with per second costs, or for long-running workloads allowing you to build in high availability into your architecture.

EMR is based on the popular and solid Apache Hadoop framework, an open-source distributed processing framework intended for big data processing. Organizations and companies can gain great benefit in using Amazon EMR because it abstracts and reduces the complexity of the infrastructure layer used with traditional MapReduce frameworks.

The efforts involved in implementing a healthy Hadoop cluster setup are not so trivial. So what AWS did was to encapsulate all the infrastructure of the Hadoop framework into an integrated environment so you can launch a cluster in minutes and focus on the real important part, which is not managing infrastructure but getting your data processed according to your needs.

Amazon EMR securely and reliably handles your data analytic use cases, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. Amazon EMR takes advantage of Amazon EC2 instances that are configured with the Hadoop framework to deliver petabyte-scale processing power. 

Amazon EMR supports a number of other frameworks used within the field of big data and data analytics, these include Spark, Presto, and HBase.  Using the AWS Management Console or AWS CLI, you can quickly and easily create clusters for each of these frameworks. 

About the Author
Students
37985
Courses
26
Learning Paths
20

Danny has over 20 years of IT experience as a software developer, cloud engineer, and technical trainer. After attending a conference on cloud computing in 2009, he knew he wanted to build his career around what was still a very new, emerging technology at the time — and share this transformational knowledge with others. He has spoken to IT professional audiences at local, regional, and national user groups and conferences. He has delivered in-person classroom and virtual training, interactive webinars, and authored video training courses covering many different technologies, including Amazon Web Services. He currently has six active AWS certifications, including certifications at the Professional and Specialty level.