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, Amazon QuickSight, Amazon Athena and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.
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 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
Leverage AWS Glue to automate ETL workloads.
Use visualization software to depict data and queries using Amazon QuickSight
Orchestrate big data workflows using AWS Data Pipeline
Individuals responsible for designing and implementing big data solutions, namely Solutions Architects and SysOps Administrators.
Data Scientists and Data Analysts interested in learning about big data solutions on AWS.
We recommend that attendees of this course have the following prerequisites: