Databricks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Interviewed for a data engineering role at Databricks and got hit with some architecture fundamentals pretty early on. Nothing too wild but it made me realize I'd been fuzzy on a few distinctions I thought I had locked down.

Questions Asked (1)

What is the difference between a data lakehouse and a data warehouse, and when would you choose one over the other?

System DesignTechnical Trade-offsData Modeling

Author's notes

I had a decent answer for the first part but stumbled a bit on the tradeoffs portion.

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AI HintsAI Generated

Suggested Approach

Start by defining both concepts clearly, then contrast their architectures, capabilities, and trade-offs. Finally, explain decision criteria based on use cases, data types, and organizational needs, ideally with examples.

Pro tip: Mention that Databricks pioneered the lakehouse architecture and highlight how it unifies data engineering and analytics workloads, showing you understand the company's core value proposition.

1. Define Data Warehouse

Describe a data warehouse as a centralized repository optimized for structured data and SQL analytics, with schema-on-write and strong ACID guarantees.

2. Define Data Lakehouse

Explain that a lakehouse combines the scalability and flexibility of a data lake with the performance and governance of a warehouse, supporting diverse data types and workloads.

3. Compare Key Aspects

Contrast them on data types (structured vs. all), schema (schema-on-write vs. schema-on-read), cost, performance, and support for advanced analytics and AI.

4. Discuss Trade-offs

Highlight that warehouses offer mature tooling and performance for BI, while lakehouses reduce data duplication and enable unified governance across all data.

5. Provide Decision Criteria

Explain when to choose each: warehouse for traditional BI with structured data and strict SLAs; lakehouse for unified analytics, ML, and diverse data at scale.

Key Points to Mention

Schema-on-write vs. schema-on-read
Support for structured, semi-structured, and unstructured data
ACID transactions and governance in lakehouses
Cost and scalability differences
Use cases: BI reporting vs. machine learning and advanced analytics
Databricks' lakehouse platform and Delta Lake

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.