2 posts tagged with "lakehouse"

The data lakehouse is built on a deceptively simple idea: add a metadata and transaction layer on top of cheap object storage, and you get warehouse-grade reliability at lake-scale costs. The table format is that layer — and three formats dominate the market: Apache Iceberg, Delta Lake, and Apache Hudi.

All three solve the same core problem. The differences in how they solve it have real consequences for query performance, streaming support, tooling compatibility, and operational complexity. This post cuts through the marketing to give you a technical basis for choosing.

Every modern data strategy starts with the same question: where does the data live, and in what form? The answer determines everything downstream — what analytics are possible, how fast queries run, what AI workloads you can support, and how much the infrastructure costs to operate.

The three dominant paradigms — data lake, data warehouse, and data lakehouse — are often presented as competing alternatives. In practice, most mature data platforms use all three in combination. Understanding what each is optimized for helps you decide which layer owns which data at each stage of its lifecycle.