Building Data Analytics Solutions Using Amazon Redshift

Description

Overview:

The course dives deep into the Amazon Redshift service and the current thinking in building and operating data analytics pipelines to turn data into insights. You can ask questions and get real-time feedback from expert AWS instructors to immediately apply to your data analytics solutions. You’ll build a data warehouse analytics workflow based on a real-world scenario with the content learned through tutorials, hands-on labs, discussion, demonstration, presentations, and group exercises.

Course Objectives:

• Compare the features and benefits of data warehouse, data lakes, and modern data architectures
• Design and implement a data warehouse analytics solution
• Identify and apply appropriate techniques, including compresssion 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

Prerequisites:

• Completed either AWS Technical Essentials or Architecting on AWS
• Completed Building Data Lakes on AWS

Audience:

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

Course Outline:

Module A: Overview of Data Analytics and the Data Pipeline

Module 1: Using Amazon Redshift in the Data Analytics Pipeline

Module 2: Introduction to Amazon Redshift

• Lab 1: Load and Query Data in an Amazon Redshift Cluster

Module 3: Ingestion and Storage

• Lab 2: Data Analytics Using Amazon Redshift Spectrum

Module 4: Processing and Optimizing Data

• Lab 3: Data Transformations in Amazon Redshift

Module 5: Security and Monitoring of Amazon Redshift Clusters

Module 6: Designing Data Warehouse Analytics Solutions

Module B: Developing Modern Data Architectures on AWS