Goals
In this course, you will learn how to:- Evaluate the relationship between Amazon Redshift and other Big Data systems- Evaluate use cases for data warehousing workloads and review real-world 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- Understand which security features are appropriate for 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 designmethods- Load and unload data and perform data maintenance tasksContents
In this course, you will learn concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. We will demonstrate how to collect, store, and prepare data for the data warehouse by using other AWS services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, and Amazon Simple Storage Service (Amazon S3). We will also explore how to use business intelligence (BI) tools to perform analysis on your data.Prerequisites
This course is intended for:- Database architects- Database administrators- Database developers- Data analysts - Data scientistsWe recommend that attendees of this course have the following prerequisites:- AWS Technical Essentials or equivalent experience with AWS- Familiarity with relational databases and database design concepts
