Data Lakehouse & Data Fabric

The goal of a Data Lakehouse is to provide a single, coherent platform for storing, managing, analyzing, and sharing data of any scope and structure, from raw and unstructured to highly organized and structured data.

At its core, the Lakehouse model combines cost-effective data storage and broad data compatibility of a Data Lake with transaction support (ACID transactions), data management features, and optimizations for analytical workloads traditionally associated with Data Warehouses. This enables organizations to perform complex data analysis, machine learning, and AI workloads directly on the data stored in the Data Lake, without the need for extensive data movement or transformation.

Implementing a Data Lakehouse allows companies to benefit from a simplified data architecture that provides a unified view of all data, improves governance and security, while maintaining flexibility for future data requirements. It supports a wide range of data processing and analysis tasks, and enables users to easily derive valuable insights from their data, regardless of its complexity or volume.

While a Data Lakehouse aims to combine the benefits of Data Lakes and Data Warehouses within a single architecture, a Data Fabric focuses on connecting and orchestrating these and other data resources across all system boundaries. A central role is played by the Data Catalog, which in a Data Fabric is enhanced with complex AI and Knowledge Graph functionalities. The Data Catalog stores all information about all systems and can decide itself how and from where to pull the data for the respective query. Data Fabric is currently a hot topic. However, it is important to understand that significant development work remains in this area and some approaches still need to prove themselves. Nonetheless, the Data Lakehouse serves as an optimal starting point. Any Data Lakehouse architecture can also be easily expanded into a Data Fabric in the future.