Data Warehouse vs. Data Lake vs. Lakehouse

WHAT THIS TESTS: Your knowledge of large-scale data management patterns. The interviewer wants to see if you understand the 'why' behind these architectures—the business needs and technical trade-offs that drive their design. Can you articulate the problems solved by each (BI vs. ML) and the pain points that led to the lakehouse concept, such as data silos, redundant costs, and stale data?

A GOOD ANSWER COVERS: First, define a Data Warehouse as a system optimized for Business Intelligence (BI) and reporting. It uses highly structured, processed data and a 'schema-on-write' model, making it reliable but relatively inflexible and expensive. Second, contrast this with a Data Lake, a central repository for raw data in any format. It uses a 'schema-on-read' model, making it flexible and low-cost, which is ideal for data science and machine learning, but can become an ungoverned 'data swamp'. Third, introduce the Lakehouse as a hybrid architecture that combines the best of both. It is built on low-cost data lake storage but adds a transactional metadata layer (like Delta Lake or Apache Iceberg). This layer provides critical warehouse-like features directly on the lake, including ACID transactions, schema enforcement and evolution, and unified governance. The primary benefit is creating a single source of truth for all workloads—from BI to ML—eliminating data duplication and improving freshness.

COMMON WRONG ANSWERS: Vaguely describing a lakehouse as just 'a faster data lake' without mentioning the reliability and governance features. Simply listing features without explaining the trade-offs, for example, saying 'warehouses are for SQL, lakes are for Python' without explaining the underlying schema and data structure reasons. Failing to name the key enabling technologies; a senior answer mentions the transactional layer (Delta Lake, Iceberg, Hudi) as the core innovation. Confusing the 'medallion architecture' (bronze, silver, gold tables) with the lakehouse itself; the medallion pattern is a method for organizing data within a lakehouse, not the definition of it.

LIKELY FOLLOW-UPS: When would you still recommend a traditional data warehouse over a lakehouse? What are the core technical components that enable a lakehouse? Can you describe the 'medallion architecture' and explain how it helps prevent a data swamp?

ONE CONCRETE EXAMPLE: A company ingests raw, real-time clickstream data (JSON) into its 'bronze' layer in a data lake. A scheduled job cleans, de-duplicates, and validates this data, saving it as a Delta table in the 'silver' layer. This provides an ACID-compliant, versioned, single source of truth. From this silver table, two things happen: first, an analytics team builds an aggregated 'gold' table to power a BI dashboard showing user engagement metrics. Second, a data science team uses the same silver table to train a recommendation engine. Both BI and ML workloads operate on the same, consistent data without data movement or duplication.