Big Data on AWS

About the course

In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Pig and Hive. 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 cost-effectiveness.

ideal for

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

Objectives

After completing this course, students will be able to:

• Understand Apache Hadoop in the context of Amazon EMR

• Understand the architecture of an Amazon EMR cluster

• Launch an Amazon EMR cluster using an appropriate Amazon Machine Image and Amazon EC2 instance types

• Choose appropriate AWS data storage options for use with Amazon EMR

• Know your options for ingesting, transferring, and compressing data for use with Amazon EMR

• Use common programming frameworks available for Amazon EMR including Hive, Pig, and Streaming

• Work with Amazon Redshift to implement a big data solution

• Leverage big data visualization software

• Choose appropriate security options for Amazon EMR and your data

• Perform in-memory data analysis with Spark and Shark on Amazon EMR

• Choose appropriate options to manage your Amazon EMR environment cost-effectively

• Understand the benefits of using Amazon Kinesis for big data

Prerequisite Knowledge

• Basic familiarity with big data technologies, including Apache Hadoop and HDFS

• Knowledge of big data technologies such as Pig, Hive, and MapReduce is helpful but not required

• Working knowledge of core AWS services and public cloud implementation

• Students should complete the AWS Essentials course or have equivalent experience

• Basic understanding of data warehousing, relational database systems, and database design