Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

This course is designed to teach you how to:

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
Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3, to contribute to the data warehousing solution
Architect the data warehouse
Identify performance issues, optimize queries, and tune the database 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

  • Module 1: Introduction to Data Warehousing

  • Relational databases
    Data warehousing concepts
    The intersection of data warehousing and big data
    Overview of data management in AWS
    Hands-on lab 1: Introduction to Amazon Redshift

  • Module 2: Introduction to Amazon Redshift

  • Conceptual overview
    Real-world use cases
    Hands-on lab 2: Launching an Amazon Redshift cluster

  • Module 3: Launching clusters

  • Building the cluster
    Connecting to the cluster
    Controlling access
    Database security
    Load data
    Hands-on lab 3: Optimizing database schemas

  • Module 4: Designing the database schema

  • Schemas and data types
    Columnar compression
    Data distribution styles
    Data sorting methods

  • Module 5: Identifying data sources

  • Data sources overview
    Amazon S3
    Amazon DynamoDB
    Amazon EMR
    Amazon Kinesis Data Firehose
    AWS Lambda Database Loader for Amazon Redshift
    Hands-on lab 4: Loading real-time data into an Amazon Redshift database

  • Module 6: Loading data

  • Preparing Data
    Loading data using COPY
    Data Warehousing on AWS
    AWS Classroom Training
    Concurrent write operations
    Troubleshooting load issues
    Hands-on lab 5: Loading data with the COPY command

  • Module 7: Writing queries and tuning for performance

  • Amazon Redshift SQL
    User-Defined Functions (UDFs)
    Factors that affect query performance
    The EXPLAIN command and query plans
    Workload Management (WLM)
    Hands-on lab 6: Configuring workload management

  • Module 8: Amazon Redshift Spectrum

  • Amazon Redshift Spectrum
    Configuring data for Amazon Redshift Spectrum
    Amazon Redshift Spectrum Queries
    Hands-on lab 7: Using Amazon Redshift Spectrum

  • Module 9: Maintaining clusters

  • Audit logging
    Performance monitoring
    Events and notifications
    Lab 8: Auditing and monitoring clusters
    Resizing clusters
    Backing up and restoring clusters
    Resource tagging and limits and constraints
    Hands-on lab 9: Backing up, restoring and resizing clusters

  • Module 10: Analyzing and visualizing data

  • Power of visualizations
    Building dashboards
    Amazon QuickSight editions and feature


This course is intended for Database architects, Database administrators, Database developers, or Data analysts and scientists.




We recommend that attendees of this course have the following prerequisites: Familiarity with relational databases and database design concepts


Length: 3.0 days (24 hours)


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