In this course, you will learn how to design, deploy, load, manage, and query data warehouses and data marts. You will begin by understanding different analytics repositories including data marts, data warehouses, data lakes, data lakehouses and their functions and uses. They are designed to enable rapid business decision-making through accurate and flexible reporting and data analysis.

A data warehouse is one of the most fundamental business intelligence tools in use today, and one that successful Data Engineers must understand. Additionally, you will learn to design, model, and implement data warehouses and explore data warehousing architectures such as Star and Snowflake schemas. Finally, you will learn how to populate data warehouses using Extract, Transform, and Load (ETL) and Extract, Load, Transform (ELT) processes, verify data, query data, and use Cubes, Rollups, and materialized views/tables.

The hands-on labs in this course will enable you to apply what you learn and gain a practical knowledge of data warehousing. You will work with repositories such as PostgreSQL and IBM Db2.

Course Syllabus

Module 1: Data Warehouses, Data Marts, and Data Lakes
Module 2: Designing, Modeling, and Implementing Data Warehouses
Module 3: Final Assignment and Final Quiz
Module 4 (Optional): IBM Db2

What you'll learn

Discuss the fundamentals of data warehouses, data marts, and data lakes by analyzing their features and advantages.
Examine organizations' decision-making processes to select specific data warehouse systems and vendors.
Design and populate a data warehouse, and model and query data using Cubes, Rollups, and materialized views.
Design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.

Prerequisites

To succeed in this course, it is recommended that you should have a fundamental knowledge of Relational Database Management Systems (RDBMS) and a working knowledge of Structured Query Language (SQL).

Data Warehouse Fundamentals

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