Data Warehousing on AWS - Overview

This course teaches the fundamentals and best practices for building a cloud-based data warehouse using Amazon Redshift. It covers how to collect, store, and process data efficiently while explaining Redshift’s features and its role in solving business and technical challenges. You will also learn solution design using the Well-Architected Framework, along with integration, performance optimization, orchestration, security, and monitoring.

After completing Data Warehousing on AWS, students will be able to:

  • Explain how Amazon Redshift fits into a modern data architecture
  • Create and manage a cloud-based data warehouse solution
  • Import and organize data from diverse data sources
  • Apply best practices for security, optimization, and backup planning
  • Enable secure data sharing across teams and clusters
  • Automate data workflows using orchestration tools like AWS Step Functions
  • Build and deploy machine learning models directly within the data warehouse

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Data Warehousing on AWS - Key Features

  • Data Warehousing on AWS has hands-on labs to encourage Thinking-Based Learning (TBL).

  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).

  • This is taught by AWS Authorized Instructors to develop Competency-Based Learning (CBL).

  • Well-structured use-cases to simulate challenges encountered in a Real-World environment.

  • Being an authorized AWS Training Partner gives us an edge over competition.

Who Should Attend?

  • Database Architects
  • Database Administrators
  • Database Developers
  • Data Analysts
  • Data Scientists

Prerequisites -

We recommend that attendees of this course have:

  • Taken AWS Technical Essentials (or equivalent experience with AWS)
  • Familiarity with relational databases and database design concepts

In this course, you will:

  • Discuss the core concepts of data warehousing, and the intersection between data warehousing and big data solutions
  • Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud.
  • Identify performance issues, optimize queries, and tuning for better performance.
  • Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket
  • Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse.

Data Warehousing on AWS - Course Outline Download Course Outline

  • Modern data architecture
  • Introduction to the course story
  • Data warehousing with Amazon Redshift
  • Amazon Redshift Serverless architecture
  • Hands-On Lab-1: Launch and Configure an Amazon Redshift Serverless Data Warehouse

  • Data models for Amazon Redshift
  • Data management in Amazon Redshift
  • Managing permissions in Amazon Redshift
  • Hands-On Lab-2: Setting up a Data Warehouse using Amazon Redshift Serverless

  • Overview of data sources
  • Loading data from Amazon Simple Storage Service (Amazon S3)
  • Extract, transform, and load (ETL) and extract, load, and transform (ELT)
  • Loading streaming data
  • Loading data from relational databases
  • Hands-On Lab-3: Populating the data warehouse

  • Features of Amazon Redshift Query Editor v2
  • Demonstration: Using Amazon Redshift Query Editor v2
  • Advanced queries
  • Hands-On Lab-4: Data Wrangling on AWS

  • Disaster recovery
  • Backing up and restoring Amazon Redshift provisioned
  • Backing up and restoring Amazon Redshift Serverless

  • Factors that impact query performance
  • Table maintenance and materialized views
  • Query analysis
  • Workload management
  • Tuning guidance
  • Amazon Redshift monitoring
  • Hands-On Lab-5: Performance Tuning the Data Warehouse

  • Introduction to Amazon Redshift security and compliance
  • Authentication with Amazon Redshift
  • Access control with Amazon Redshift
  • Data encryption with Amazon Redshift
  • Auditing and compliance with Amazon Redshift
  • Hands-On Lab-6: Securing Amazon Redshift

  • Overview of data orchestration
  • Orchestration with AWS Step Functions
  • Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA)
  • Hands-On Lab-7: Orchestrating the Data Warehouse Pipeline

  • Machine Learning Overview
  • Getting started with Amazon Redshift ML
  • Amazon Redshift ML workflow scenarios
  • Amazon Redshift ML Usage
  • Hands-On Lab-8: Predicting customer churn with Amazon Redshift ML

  • Overview of data sharing in Amazon Redshift
  • Amazon DataZone for Data as a service

  • Hands-On Lab-9: End of course challenge lab

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Course ID: 13499

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