data warehouse vs data lakes vs data lakehouse
Overview of Comparison of capabilities of Data Warehouse vs Data Lakes vs Data Lakehouse
| Capabilities | Data Warehouse | Data Lake | Data Lakehouse |
| Use Cases | BI and Reporting | Supports many use cases — not just data science and ETL — in modern and mature environments | Unified Platform which supports BI, Data science, ML and AI workloads |
| Low latency and high concurrency workloads | Supports, for example, operational reporting, self-service data, data sharing, customer 360 and archiving | ||
| Data Format | Closed proprietary format | Open format | Open format |
| Data Type | Structured data, with limited support for semistructured data | All types: Structured data, semistructured data, textual data, unstructured (raw) data | All types: Structured data, semistructured data, textual data, unstructured (raw) data |
| Data Access | SQL only | Open APIs for direct access to files with SQL, R, Python and other languages | SQL, along with API extensions to access tables and data |
| Reliability | High quality — reliable data with ACID transactions | Low quality — becomes a data swamp if implemented without data catalogs and the right use cases and governance | High quality — reliable data with ACID transactions |
| Governance and Security | Fine-grained security and governance at the row/column level for tables | Poor governance, as security needs to be applied to files | Fine-grained security and governance at the row/column level for tables |
| Performance | High | Low | High |
| Scalability | Scalable but expensive | Highly Scalable with low cost | Scales to hold any amount of data at low cost, regardless of type |
| Streaming | Partial; limited scale | Yes | Yes |