Course Objectives

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

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

Course Content

• Module A: Overview of Data Analytics and the Data Pipeline
• Module 1: Introduction to Amazon EMR
• Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage
• Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR
• Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive
• Module 5: Serverless Data Processing
• Module 6: Security and Monitoring of Amazon EMR Clusters
• Module 7: Designing Batch Data Analytics Solutions
• Module B: Developing Modern Data Architectures on AWS

You can find more information about this training on our website.