Benchmarking database architectures : Data Warehouse, Data Lake and Data Lakehouse

Database architectures have been subject to constant innovation, evolving with the appearance of new use cases, technical constraints and requirements. Of the three database structures we compare, the first to emerge was Data Warehouses, with the support of OnLine Analytical Processing (OLAP) systems, helping organizations cope with the increase in diverse applications by centralizing and supporting historical data to achieve competitive business analytics. Later, Data Lakes emerged, thanks to innovations in Cloud Computing and Storage, allowing exorbitant amounts of data to be saved in various formats for future analysis.

Even today, both solutions remain popular depending on different business needs. For example, Data Warehouses allow for high performance business analysis and fine-grained Data Governance. However, they lack scalability at an affordable price for petabytes of data. On the other hand, Data Lakes enable high throughput and low latency, but they pose Data Governance challenges, leading to unmanageable data swamps. In addition, data is considered immutable, which leads to additional integration efforts.

As a result, we can see the modern Data Lake and Data Warehouse ecosystems converging, each drawing inspiration, borrowing concepts and addressing use cases from the other. Within this landscape, we see a new architecture emerging: the Data Lakehouse, which attempts to combine the key benefits of both competing architectures…