Lakehouse Architecture: Unifying Data Lake Flexibility and Data Warehouse Reliability Through Delta Lake, Apache Iceberg, and Apache Hudi Transaction Layers
The traditional separation of enterprise data platforms into analytical data warehouses and raw data lakes -- each optimized for different workload types and managed by distinct teams -- has created organizational and technical friction that impedes time-to-insight for analytical consumers. The Lakehouse architecture, which adds ACID transaction semantics, schema enforcement, and time travel capabilities to data lake storage through open table format layers, promises to unify these paradigms. This paper presents the first systematic academic evaluation of Lakehouse architectures, comparing three leading open table format implementations -- Delta Lake, Apache Iceberg, and Apache Hudi -- across six operational dimensions: ACID transaction correctness, concurrent write throughput, schema evolution flexibility, time travel query performance, storage efficiency, and ecosystem compatibility. Evaluations are conducted on a 50-node Spark cluster processing a 20TB synthetic dataset with real-world distribution characteristics derived from a financial institution data platform. Delta Lake achieves the highest concurrent write throughput (340 transactions/second) and strongest ecosystem compatibility. Iceberg demonstrates superior schema evolution flexibility and cross-engine portability. Hudi delivers the lowest storage overhead for change-heavy workloads through its record-level upsert optimization. We introduce the Lakehouse Platform Fitness Score (LPFS) and provide a selection framework based on workload mix, team expertise, and ecosystem lock-in tolerance.
