Skip to content

Latest commit

 

History

History

Course 2-Cloud Data Warehouses

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
Project 2-Data Warehouse
Project 2-Data Warehouse
 
 
README.md
README.md
 
 

README.md

Course 2 - Could Data Warehouses

Gain experience in a wide range of skills, including:

  • Data warehouse architecture
  • Extracting, transforming, and loading data (ETL)
  • Cloud data warehouses
  • AWS Redshift
  • Amazon S3

Lesson 1 - Introduction to Data Warehouses

Learn the business case for data warehouses as well as architecture, extracting, transforming, and loading data, data modelling, and data warehouse technologies.

  • Explain how OLAP may support certain business users better than OLTP
  • Implement ETL for OLAP Transformations with SQL
  • Describe Data Warehouse Architecture
  • Describe OLAP cube from facts and dimensions to slice, dice, roll-up, and drill down operations
  • Implement OLAP cube from facts and dimensions to slice, dice, roll-up, and drill down
  • Compare columnar vs. row-oriented approaches
  • Implement columnar vs. row-oriented approaches

Lesson 2 - ELT and Data Warehouse Technology in the Cloud

Learn about ELT, the differences between ETL and ELT, and general cloud data warehouse technologies.

  • Explain the differences between ETL and ELT
  • Differentiate scenarios where ELT is preferred over ETL
  • Implement ETL for OLAP Transformations with SQL
  • Select appropriate cloud data storage solutions
  • Select appropriate cloud pipeline solutions
  • Select appropriate cloud data warehouse solutions

Lesson 3 - AWS Data Technologies

Learn about AWS Services and how to set up Amazon S3, IAM, VPC, EC2, and RDS. Build a Redshift data warehouse cluster and learn how to interact with it.

  • Describe AWS data warehouse services and technologies
  • Create and configure AWS Storage Resources
  • Create and configure AWS Redshift Resources
  • Implement infrastructure as code for Redshift on AWS

Lesson 4 - Implementing Data Warehouses on AWS

  • Describe Redshift data warehouse architecture
  • Run ETL process to extract data from AWS S3 into Redshift
  • Design optimized tables by selecting appropriate distribution styles and sorting keys

Project - Cloud Data Warehouse

Build an ETL pipeline that extracts data from S3, stages data in Redshift, and transforms data into a set of dimensional tables for an analytics team.