Audience Profile

This course is intended for:

• Data platform engineers
• Architects and operators who build and manage data analytics pipelines
• Data warehouse engineers

Prerequisites

Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.

We suggest the AWS Hadoop Fundamentals course for those that need a refresher on Apache Hadoop.

We recommend that attendees of this course have:

• Completed either AWS Technical Essentials or Architecting on AWS
• Completed either Building Data Lakes on AWS or Getting Started with AWS Glue

At Course Completion

In this course, you will learn to:

• Compare the features and benefits of data warehouses, data lakes, and modern data architectures
• Design and implement a batch data analytics solution
• Identify and apply appropriate techniques, including compression, to optimize data storage
• Select and deploy appropriate options to ingest, transform, and store data
• Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
• Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
• Secure data at rest and in transit
• Monitor analytics workloads to identify and remediate problems
• Apply cost management best practices