Data Warehousing on AWS

Part 1: 

1. Introduction to Data Warehousing
2. Introduction to Amazon Redshift
3. Launching Clusters

Part 2: 

1. Designing the Database Schema
2. Identifying Data Sources
3. Loading Data

Part 3:

1. Writing Queries and Tuning Performance
2. Amazon Redshift Spectrum
3. Maintaining Clusters
4. Analyzing and Visualizing Data

• Database architects
• Database administrators
• Database developers
• Data analysts and scientists

• 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.

A full refund will be issued for class cancellations made at least 15 business days before the course begins. Payment is non‑refundable for cancellations or reschedules made within 15 business days from the course start date and for No‑Shows (students who do not attend class).
For reschedules made within 15 business days from the course start date, students must reschedule immediately for a current, published course, up to a maximum of 90 days from the original date.
A student may reschedule a class or exam up to 2 times. Any additional reschedules will not be allowed.