# Modern Data Architecture (Data Engineering) > Data Lake vs Data Warehouse vs Lakehouse, Data Mesh, Data Contracts, schema registry, ADR, governance, data catalog, lineage - 20 interview questions - Senior - [Interview Questions: Data Engineering](https://sharpskill.dev/en/technologies/data-engineering/interview-questions.md) ## 1. What is the fundamental difference between a Data Lake and a Data Warehouse? **Answer** A Data Lake stores data in its native (raw) format with schema applied at read time (schema-on-read), allowing great flexibility for exploration. A Data Warehouse enforces a structured schema at write time (schema-on-write) with transformed data optimized for analytics. Data Lakes favor flexibility and massive low-cost storage, while Data Warehouses favor query performance and data quality. ## 2. What is the main advantage of Lakehouse architecture compared to separate Data Lake and Data Warehouse architectures? **Answer** Lakehouse architecture combines the best of both worlds: the flexible and cost-effective storage of Data Lake with ACID capabilities, query performance, and governance of Data Warehouse. This eliminates data duplication between systems, reduces synchronization costs and complexity, while enabling BI and ML workloads on the same platform using open formats like Delta Lake, Iceberg, or Hudi. ## 3. Which open table format enables ACID transactions on a Data Lake? **Answer** Delta Lake, Apache Iceberg, and Apache Hudi are the three main open table formats enabling ACID transactions on a Data Lake. Delta Lake, developed by Databricks, uses a transaction log to guarantee atomicity and consistency. Iceberg, created by Netflix, offers advanced partition management and schema evolution. Hudi, developed by Uber, excels in upsert and CDC scenarios. These formats transform simple object storage into a Lakehouse with transactional guarantees. ## 17 more questions available - What is the fundamental principle of Data Mesh? - What is a Data Contract in the context of Data Mesh? 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