| Primary Architecture | Raw, low-cost object storage (e.g., AWS S3, Azure Blob) designed to store unformatted files, logs, and unstructured data in a flat file hierarchy. | A unified architectural layer built on top of cloud object storage that combines data lake storage with a transactional storage framework (e.g., Delta Lake, Apache Iceberg). |
| ACID Transactions | Not supported: Concurrent reads and writes often lead to corrupted data, half-written tables, or read inconsistencies. | Fully supported: Guarantees atomicity, consistency, isolation, and durability (ACID) for safe concurrent reads and writes. |
| Schema & Data Quality | Schema-on-read: Data is dumped in its raw form without strict structural rules, often turning into an unmanaged "data swamp." | Schema enforcement & evolution: Enforces strict data types, validation rules, and quality checks at ingestion while supporting safe schema changes. |
| Performance & Indexing | Slow for complex SQL queries and BI reporting because it lacks advanced indexing and statistics collection. | Fast query performance comparable to data warehouses, utilizing file-level statistics, caching, and partitioning (Z-ordering / layout optimization). |
| Workload Support | Ideal primarily for data science, machine learning, and raw storage archiving. | Supports all workloads simultaneously: BI reporting, SQL analytics, streaming, data science, and machine learning on a single copy of data. |
| Open Formats & Lock-in | Proprietary or raw file formats (CSV, JSON, plain Parquet) lacking transaction history tracking. | Uses open storage formats wrapped with transaction logs (Parquet + Delta/Iceberg metadata), preventing vendor lock-in. |
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