Data Warehousing Concepts

Course Overview

This DataCamp course walks you through the core ideas behind data warehousing without requiring you to write a single query. It's built around video lessons, light reading, and interactive exercises that use real datasets, so you get a feel for how warehousing decisions play out in actual business contexts rather than just memorizing definitions.

The course spends real time contrasting things people often confuse: data warehouses versus data lakes versus data marts, OLAP versus OLTP, ETL versus ELT, and the Inmon top-down approach versus Kimball's bottom-up method. By the end, you're expected to be able to reason through design choices — like when to use a star schema versus a snowflake schema, or when a cloud deployment makes more sense than an on-premise one — rather than just recite vocabulary.

What You Will Learn

• The difference between data warehouses, data lakes, and data marts, and when each fits a given use case
• How a data warehouse is structured in layers, including the role of the presentation layer
• Inmon's top-down methodology versus Kimball's bottom-up methodology, and how each affects data flow and normalization
• OLAP versus OLTP systems and how to decide which fits a given scenario
• Star and snowflake schema design, including choosing fact tables, dimension tables, and handling slowly changing dimensions (Types I, II, III)
• Row-store versus column-store storage and why column-store tends to be faster for certain queries
• ETL versus ELT processes and basic data cleaning and governance concepts
• On-premise versus cloud warehouse implementations and the tradeoffs involved

Course Structure

The course is organized into four chapters, each mixing short videos with scored exercises:

1. Data Warehouse Basics — defining a data warehouse, comparing it to data lakes and marts, and introducing the project lifecycle and team roles.
2. Warehouse Architectures and Properties — warehouse layers, ETL basics, Inmon vs. Kimball architectures, and OLAP vs. OLTP systems.
3. Data Warehouse Data Modeling — fact and dimension tables, star/snowflake schemas, Kimball's four-step modeling process, slowly changing dimensions, and row vs. column storage.
4. Implementation and Data Prep — ETL vs. ELT, data cleaning and governance, on-premise vs. cloud deployment, and a closing design scenario that ties the concepts together.

A final graded assessment lets you earn CPE credits if you score 70% or higher.

Who Is This Course For?

This fits people who need to understand data warehousing conceptually — analysts, aspiring data engineers, project managers, or anyone evaluating or working near a data warehouse project — without necessarily building one from scratch themselves. It's reasonable for someone early in their data career, since it assumes only basic SQL familiarity.

If you already work hands-on with warehouse architecture, have designed star schemas before, or are looking for a course that has you writing SQL or building a warehouse end-to-end, this will likely feel too basic. It's a concepts primer, not a hands-on build project.

Format & Time Commitment

The course is self-paced with no deadlines, structured as 16 videos plus 57 short exercises adding up to about 4 hours of total content. Videos include transcripts you can reveal on screen, and there's a glossary available alongside the lessons if terminology gets confusing.

Pros and Cons

Pros

• Clear, structured comparisons between frequently confused concepts (warehouse vs. lake vs. mart, OLAP vs. OLTP, ETL vs. ELT)
• Short, digestible format that fits around a busy schedule
• Includes a glossary and video transcripts for accessibility
• Offers CPE credit eligibility for professionals who need it

Cons

• Entirely conceptual — there are no hands-on SQL or warehouse-building exercises, so it won't build practical implementation skills on its own
• At roughly 4 hours, coverage of each topic (especially modeling techniques like slowly changing dimensions) stays fairly surface-level
• Requires a DataCamp subscription or course purchase to access, so standalone pricing isn't transparent on the page itself
• The completion certificate is a "Statement of Accomplishment" rather than an industry-recognized certification, so its weight on a resume is limited

FAQ

Is this course beginner-friendly? Yes — it only assumes you've completed an Introduction to SQL course and don't yet know data warehousing terminology or architecture.

Will I write any SQL or build a warehouse in this course? No. It's a conceptual course built around videos and quiz-style exercises, not a hands-on implementation project.

Is the certificate worth something on a resume? It's a Statement of Accomplishment you can share on LinkedIn, plus optional CPE credits if you pass the assessment — useful as a learning record, but it's not an industry certification like DataCamp's separate Data Engineer certification track.

What should I take instead if I want hands-on practice? If you already know the concepts and want to practice building schemas or running ETL/ELT pipelines, look for a project-based or SQL-heavy warehousing course instead.

If data warehousing has always felt like a wall of confusing acronyms, this course is a reasonable place to go clear that up before diving into something more hands-on.