--- title: "Data Lakehouse Fundamentals" description: "Learn the core concepts of the data lakehouse architecture: open table formats, how it compares to data warehouses and data lakes, and lakehouse strategy." scfAsset: managed: false category: "guide" maintainers: - user: "can.celik1" external: true tags: ["STACKIT University", "Learning", "Lakehouse", "Open Table Formats", "Architecture"] source_url: "https://framework.stackit.cloud/data-and-ai/assetcontainer/stackit/data-lakehouse-fundamentals/" source_file: "docs/data-and-ai/assetcontainer/stackit/data-lakehouse-fundamentals.mdx" --- ## Course Overview This course introduces the data lakehouse architecture and the open table formats that underpin it, working through what a lakehouse actually is, how it differs architecturally from a traditional data warehouse or a plain data lake, and where each approach still makes sense on its own. It's a foundational, vendor-agnostic course: the goal is to leave you able to reason about the trade-offs between these architectures before you commit to a specific implementation. That grounding matters because it's a prerequisite for the more specialized data engineering courses that follow, including the Apache Iceberg and Dremio deep dives — this is where you build the conceptual map before working with a specific table format or query engine. The closing module turns theory into decision-making, walking through how to evaluate and build a lakehouse strategy for your own organization rather than adopting one by default. ### What You'll Learn - Explain the data lakehouse architecture and how open table formats make it possible - Compare lakehouse, data warehouse, and data lake approaches on their actual trade-offs - Recognize when each architecture is the better fit for a given workload - Evaluate the building blocks needed to define a lakehouse strategy for an organization ### Modules - Data Lakehouse Architecture - Open Table Formats - Lakehouse vs Warehouse vs Lake - Building a Lakehouse Strategy