Lakehouse Architecture – Databricks Fundamentals Course
As an enterprise Product Manager, I’m always looking for ways to deepen my understanding of the data platforms my teams and customers work with. Recently, I completed the Databricks Lakehouse Fundamentals course — a free, self-paced introduction to Databricks and the lakehouse architecture.
You can find it here: Databricks Lakehouse Fundamentals Course
Note : If you want the certificate, use the databricks academy instead.
Is it worth your time?
Short answer: Yes — especially if you’re a PM, data leader, or technologist working around data products.
Is it a little bit of a Databricks infomercial? Absolutely. But marketing aside, it’s a well-structured primer on some key concepts that are shaping modern data platforms — things like:
- The evolution from data warehouses and data lakes → to the lakehouse model
- Why open formats (like Delta Lake) matter
- How Databricks thinks about unifying data engineering, analytics, and machine learning
- And some high-level platform architecture basics
Why I recommend it (especially for Product Managers)
Most PMs working in data-heavy environments end up interfacing with data engineers, platform teams, or customers building on tools like Databricks. But the technical jargon can sometimes be a barrier.
This course won’t make you an expert — but it will give you enough context to:
- Understand what a lakehouse is (beyond the buzzword)
- Ask better questions in product conversations
- Appreciate tradeoffs in data architecture decisions
- And see where Databricks fits into the larger data ecosystem
Final Thoughts
If you’re a Product Manager (or really anyone in tech) looking to level up your data fluency, it’s a low-effort, high-reward way to invest about 1.5 hours.