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.


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

Learning Objectives

At the end of this course, you will be able to:

Discuss the core concepts of data warehousing.
Evaluate the relationship between Amazon Redshift and other big data systems.
Evaluate use cases for data warehousing workloads and review case studies that demonstrate implementation of AWS data and analytic services as part of a data warehousing solution.
Choose an appropriate Amazon Redshift node type and size for your data needs.
Discuss security features as they pertain to Amazon Redshift, such as encryption, IAM permissions, and database permissions.
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 Firehose, and Amazon S3, to contribute to the data warehousing solution.
Evaluate approaches and methodologies for designing data warehouses.
Identify data sources and assess requirements that affect the data warehouse design.
Design the data warehouse to make effective use of compression, data distribution, and sort methods.
Load and unload data and perform data maintenance tasks.
Write queries and evaluate query plans to optimize query performance.
Configure the database to allocate resources such as memory to query queues and define criteria to route certain types of queries to your configured query queues for improved processing.
Use features and services, such as Amazon Redshift database audit logging, Amazon CloudTrail, Amazon CloudWatch, and Amazon Simple Notification Service (Amazon SNS), to audit, monitor, and receive event notifications about activities in the data warehouse.
Prepare for operational tasks, such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters.
Use a business intelligence (BI) application to perform data analysis and visualization tasks against your data.

  • 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 ideal for Database architects, database administrators, database developers, and data analysts & scientists.




Familiarity with relational databases and database design concepts


Length: 3.0 days (24 hours)


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