The Open Data Lakehouse: Why Enterprises Are Walking Away from Vendor-Locked Architectures

A data engineering team initially adopts a proprietary lakehouse platform for its integrated features like managed catalog and ACID transactions but later struggles with architectural constraints that hinder multi-engine operations, such as running Flink for streaming and other engines for machine learning workloads. This highlights the critical difference between proprietary and open data lakehouses, where open systems utilize open table formats like Apache Iceberg, engine-agnostic catalogs like Apache Gravitino, and multi-engine compute frameworks without proprietary API dependencies, enabling flexibility, scalability, and interoperability across different cloud environments and workloads. Unlike traditional data warehouses optimized for structured SQL analytics with tight integration, open data lakehouses support diverse workloads, including SQL analytics and machine learning, from the same storage layer, offering schema flexibility and cost-effective storage solutions. The risks of vendor lock-in in proprietary lakehouses arise from closed catalog APIs and runtime-specific optimizations, which complicate migrations and limit interoperability, emphasizing the importance of open architecture as a strategic choice to maintain portability and reduce dependency risks over time.

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