How would you use Azure Data Factory, Databricks and Data Lake together?

08 July 2026

Question

What is your understanding of technologies such as Databricks, Azure Data Factory and Data Lake, and how do they fit together in modern data engineering?

Answer

I think of these tools as parts of one delivery pipeline rather than isolated technologies.

• Azure Data Lake Gen2: This is the storage foundation. It gives us a scalable place to keep raw, intermediate and curated data, usually structured into bronze, silver and gold layers. - Azure Data Factory: This is primarily for orchestration and movement of data. I would use it to schedule pipelines, trigger notebooks, copy data from source systems and manage dependencies between steps. - Databricks: This is where the heavier transformation and engineering work happens. I would use notebooks or jobs written in Python and SQL to clean data, apply business logic, validate outputs and build reusable tables. - Working Together: A typical pattern would be Data Factory loading source data into the bronze layer, Databricks transforming it into silver and gold layers, then curated outputs being consumed by reporting or analytics tools. - Why This Matters: This setup supports scalability, separation of concerns and better maintainability. Storage, orchestration and compute each have a clear role. For this apprenticeship, I would also stress that I am comfortable learning the detailed implementation patterns quickly, but I already understand the architectural purpose of each component and how they support reliable data products.