A Data Lakehouse is a modern data architecture that amalgamates the advantages of data lakes and data warehouses. It facilitates flexible storage of unstructured data, alongside providing management features and tools for structured data. This architecture enables both business intelligence (BI) and machine learning (ML) on all data, presenting a cost-effective and scalable data platform.
The emergence of Data Lakehouse stems from the need to harmonize the scalability, flexibility, and cost-efficiency of data lakes with the rigorous data management, ACID (Atomicity, Consistency, Isolation, Durability) transactions, and querying capabilities of data warehouses. Unlike data lakes that are often schema-on-read and more suitable for data discovery and exploration, or data warehouses that are schema-on-write and tailored for structured analytics, Data Lakehouse provides a unified data management solution. It blends the best of both worlds by offering a platform where structured and unstructured data can coexist and be analysed effectively. This hybrid model supports advanced analytics, making data easily accessible for BI and ML applications, while ensuring data quality and data governance. Data Lakehouse platforms are designed to be open, simplifying the integration with various data processing engines and tools, and are becoming increasingly popular as they offer a balanced, efficient, and unified data management solution for modern analytics needs.