Overview In this course, you will learn about cloud-based Big Data solutions such as Amazon EMR, Amazon Redshift, Amazon Kinesis, and the rest of the AWS Big Data platform. We will show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We will also teach you how to create Big Data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon QuickSight, Amazon Athena, and Amazon Kinesis, and leverage best practices to design Big Data environments for security and cost-effectiveness.
Course Objectives This course is designed to teach you how to:
Fit AWS solutions inside a Big Data ecosystem
Leverage Apache Hadoop in the context of Amazon EMR
Identify the components of an Amazon EMR cluster, then launch and configure an Amazon EMR cluster
Use common programming frameworks available for Amazon EMR, including Hive, Pig, and streaming
Improve the ease of use of Amazon EMR by using Hadoop User Experience (Hue)
Use in-memory analytics with Apache Spark on Amazon EMR
Choose appropriate AWS data storage options
Identify the benefits of using Amazon Kinesis for near real-time Big Data processing
Leverage Amazon Redshift to efficiently store and analyze data
Comprehend and manage costs and security for a Big Data solution
Identify options for ingesting, transferring, and compressing data
Leverage Amazon Athena for ad-hoc query analytics
Use AWS Glue to automate extract, transform, and load (ETL) workloads
Use visualization software to depict data and queries using Amazon QuickSight