What is a Data Lakehouse?
Blog post from Onehouse
The evolution of data architecture has led to the development of the data lakehouse, a hybrid model that combines the strengths of data warehouses and data lakes. Historically, data warehouses were designed for structured data, offering robust query capabilities but at high costs and complexity, while data lakes provided inexpensive, flexible storage for a wide range of data types but lacked the structure and accessibility of data warehouses. The data lakehouse emerged as an innovative solution to unify these systems, enabling organizations to manage large volumes of diverse data with improved performance and cost-efficiency. Key projects in this space include Apache Hudi, Apache Iceberg, and Delta Lake, each offering varying levels of openness and performance. The concept of the universal data lakehouse (UDL) aims to create an ideal architecture that leverages the best aspects of both previous systems, providing a single source of truth for data that can be processed by various compute engines. Companies like Onehouse are promoting interoperability with tools like OneTable, which supports cross-platform data management, encouraging a flexible and open approach to data architecture.
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