Big Data on AWS

Course Details

Big Data on AWS introduces you to cloud-based big data solutions such as Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis and the rest of the AWS big data platform. In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We also teach you how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, and Amazon Kinesis, and leverage best practices to design big data environments for security and costeffectiveness.

Bring Your Own Device

Please note that this class requires you to bring your own device. Plan to come to class with the following:

  • A notebook computer with Wi-Fi (labs cannot be completed on an iPad or tablet device)
  • Administrator access to the computer, if Windows
  • Chrome, Firefox, or Safari (IE not supported)

Prerequisites

We recommend that attendees of this course have the following prerequisites:

  • Basic familiarity with big data technologies, including Apache Hadoop, MapReduce, HDFS, and SQL/NoSQL querying
  • Students should complete the Big Data Technology Fundamentals web-based training or have equivalent experience
  • Working knowledge of core AWS services and public cloud implementation
  • Students should complete the AWS Technical Essentials course or have equivalent experience
  • Basic understanding of data warehousing, relational database systems, and database design

Intended Audience

This course is intended for:

  • Individuals responsible for designing and implementing big data solutions, namely Solutions Architects
  • Data Scientists and Data Analysts interested in learning about big data solutions on AWS

Hands-on Activity

This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises.

Course Objectives

This course teaches you how to:

  • Fit AWS solutions inside of a big data ecosystem
  • Leverage Apache Hadoop in the context of Amazon EMR
  • Identify the components of an Amazon EMR cluster
  • Launch and configure an Amazon EMR cluster
  • Leverage common programming frameworks available for Amazon EMR including Hive, Pig, and Streaming
  • Leverage Hue to improve the ease-of-use of Amazon EMR
  • Use in-memory analytics with Spark and Spark SQL on Amazon EMR
  • Choose appropriate AWS data storage options
  • Identify the benefits of using Amazon Kinesis for near real-time big data processing
  • Define data warehousing and columnar database concepts
  • Leverage Amazon Redshift to efficiently store and analyze data
  • Comprehend and manage costs and security for Amazon EMR and Amazon Redshift deployments
  • Identify options for ingesting, transferring, and compressing data
  • Use visualization software to depict data and queries
  • Orchestrate big data workflows using AWS Data Pipeline
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