This course is intended for:

• 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.

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 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

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

• Basic familiarity with big data technologies, including Apache Hadoop, 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 Essentials course or have equivalent experience.
• Basic understanding of data warehousing, relational database systems, and database design.