Data Warehousing on AWS
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.
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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.
Target Audience and Prerequisites
This course is intended for:
- Database architects
- Database administrators
- Database developers
- Data analysts and scientists
We recommend that attendees of this course have the following prerequisites:
- Courses taken: AWS Cloud Practitioner (or equivalent experience with AWS)
- Familiarity with relational databases and database design concepts
In this course, you will learn how to:
- Discuss the core concepts of data warehousing.
- Discuss 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.
- Evaluate approaches and methodologies for designing data warehouses.
This course covers the following concepts:
- Course Introduction
- Introduction to Data Warehousing
- Introduction to Amazon Redshift
- Understanding Amazon Redshift Components and Resources
- Launching an Amazon Redshift Cluster
- Choosing a Data Warehousing Approach
- Identifying Data Sources and Requirements
- Architecting the Data Warehouse
- Loading Data into the Data Warehouse
- Optimizing Queries and Tuning Performance
- Monitoring and Auditing the Data Warehouse
- Maintaining the Data Warehouse
- Analyzing and Visualizing Data