Data Warehousing on AWS

Description

Unlock the power of cloud-based data warehousing with the Data Warehousing on AWS course. This course equips you with the skills to design, implement, and optimize a robust data warehousing solution using Amazon Redshift. Explore Redshift’s architecture, best practices, and integration with AWS services. Learn about data ingestion, transformation, SQL analysis, disaster recovery, performance tuning, security, and access management. Dive into the potential of data sharing, workflow orchestration with Step Functions, and machine learning with Redshift ML.

Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift. This course demonstrates how to ingest, store, and transform data in the data warehouse. Topics covered include: the purpose of Amazon Redshift, how Amazon Redshift addresses business and technical challenges, features and capabilities of Amazon Redshift, designing a Data Warehousing Solution on AWS by applying best practices based on the Well-Architected Framework, integration with AWS and non-AWS products and services, performance tuning, orchestration, and securing and monitoring Amazon Redshift.

Objectives

This course teaches you how to:

• Describe Amazon Redshift architecture and its roles in a modern data architecture
• Design and implement a data warehouse in the cloud using Amazon Redshift
• Identify and load data into an Amazon Redshift data warehouse from a variety of sources
• Analyze data using SQL QEV2 notebooks
• Design and implement a disaster recovery strategy for an Amazon Redshift data warehouse
• Perform maintenance and performance tuning on an Amazon Redshift data warehouse
• Secure and manage access to an Amazon Redshift data warehouse
• Share data between multiple Redshift clusters in an organization
• Orchestrate workflows in the data warehouse using AWS Step Functions state machines
• Create an ML model and configure predictors using Amazon Redshift ML

Audience

• Data engineers
• Data architects
• Database architects
• Database administrators
• Database developers