Data Warehouse Fundamentals

Data Warehouse Fundamentals is a comprehensive course designed to provide a solid understanding of data warehousing, from basic concepts to advanced methodologies. Whether you're new to data management or looking to deepen your expertise, this course offers a structured approach to learning the essential components and architectures that make up a modern data warehouse.

The course begins with an introduction to the concept of a data warehouse (DWH), its capabilities, limitations, and the business problems it addresses. You’ll gain insight into why organizations invest in DWHs and how they help transform data into actionable insights.

As you progress, you'll explore traditional and modern approaches to data warehouse design. This includes an overview of key components such as staging areas, Operational Data Stores (ODS), Data Marts, and Business Intelligence (BI) systems. You'll also learn about different design methodologies, including foundational concepts from Inmon, Kimball, and DataVault, which offer various perspectives on data warehouse architecture.

The course also covers critical aspects of data governance, highlighting the importance of managing data as a valuable asset. You'll delve into master data and master data management (MDM), understanding how to ensure data quality, consistency, and compliance across the enterprise.

In addition, the course focuses on the techniques involved in designing a data warehouse, from engaging stakeholders to determining the infrastructure needed to support a robust DWH environment. You’ll discuss the importance of the Initial Data Store Area (Stage), compare it with a Data Lake, and analyze common pitfalls in organizing this crucial area.

Later modules will examine the various layers of permanent data storage, including ODS and Data Delivery Systems (DDS). You'll explore ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, which are critical for data retrieval, cleaning, and transformation into target storage systems.

In the final sections, the course explores how data warehouses integrate with data consumer systems, particularly BI applications. You'll gain insight into typical use cases for retrieving data from DWHs and the diverse range of BI systems available today.

Lastly, you’ll discuss the challenges of scaling data warehouses and how emerging trends such as machine learning and the Data Mesh concept are influencing the future of data warehousing.

By the end of this course, participants will:

• Understand the fundamental concepts of data warehousing and its role in business.
• Gain a strong theoretical understanding of data warehouse architectures and methodologies.
• Learn how data governance principles and master data management apply within a DWH context.
• Develop an understanding of the design and implementation considerations for scalable data warehouse systems.
• Explore emerging challenges and trends in data warehousing, including Data Mesh and machine learning.

This course primarily focuses on discussions of concepts and methodologies, offering limited hands-on practice. While it covers a wide range of problems and solutions related to data warehousing, the emphasis is on theoretical understanding and discussion rather than extensive practical exercises.

Upon completion of the "Data Warehouse Fundamentals" course, trainees will be able to:

• Design and implement a data warehouse that meets business needs and handles large-scale data effectively.
• Apply industry-standard methodologies, including Inmon, Kimball, and DataVault, in real-world projects.
• Manage enterprise data governance, ensuring data quality, consistency, and security across systems.
• Leverage modern BI systems for efficient data retrieval and reporting from a data warehouse.

Address scalability challenges and incorporate new technologies like Data Mesh and machine learning into DWH strategies.

1. Introduction. The idea of a “data warehouse.” Its capabilities and constraints. The purpose of DWH and the business tasks it solves.
2. Components and Architecture. Traditional approaches to data warehouse design. Standard components and processes. Concepts of Inmon, Kimball, and DataVault methodologies. Overview of major components (stage, ODS, DDS, Data Marts, BI, Metadata) and processes (ETL, ELT, DQ, lineage)
3. Data Governance. General and specific issues of enterprise data governance. Information as an asset that brings value and requires costs to obtain. The concept of “master data” and master data management (MDM).
4. Methods of Data Warehouse Design. Design steps. Standard techniques and tools. Stakeholders and infrastructure expertise.
5. Initial Data Store Area - Stage. Need to store initial data from the source system. Typical mistakes in organizing this store area and its difference from “Data Lake.”
6. Permanent Storage Areas - ODS and DDS. Layers of operational and multi-dimensional data storage. Retrieval, cleaning, control, and storing processes - ETL\ELT. Transformation into a target storage system.
7. Data Consumer Systems. Typical use cases of data retrieval from data warehouses.BI systems as major data warehouse consumers. Standard BI systems and reasons for their diversity.
8. New Challenges in the Evolution of Data Warehouses. Overview of major scalability problems with a data warehouse. New challenges in machine learning. The concept of Data Mesh as an alternative for future development.