Key Features and Benefits of Lakehouse Frameworks Hudi, Iceberg, and Paimon

A short note.

The lakehouse frameworks Hudi, Iceberg, and Paimon support efficient stream/batch read‑write, time‑travel, and data updates, offering capabilities that traditional real‑time and offline warehouses lack:

They provide a native unified batch‑stream storage engine, allowing full‑table batch access and incremental changelog stream processing.

They support UPSERT streams and use a more efficient LSM‑based file organization.

They enable TimeTravel, theoretically allowing batch or stream processing from any point in time.

They also support other offline‑warehouse operations.

If we build a new data‑warehouse system—called a Streaming Warehouse—on top of these lake frameworks, all development can target tables using pure SQL.

This architecture addresses core challenges:

When performance is sufficient, it can achieve latency comparable to real‑time pipelines.

It offers native batch‑stream integration with consistent semantics, ensuring data consistency.

Intermediate results are persisted and queryable, a major advantage over many current real‑time warehouses.

Historical data repair becomes straightforward.

Development and storage costs are low.

Many articles highlight that this approach achieves unified batch‑stream computation and storage, supporting stream, batch, and OLAP processing, and handling data as a "Table".

Current replaceable scenarios include workloads that can tolerate minute‑level end‑to‑end latency, require strong consistency between complex offline and real‑time logic, and traditionally rely on databases with materialized views or stored procedures for online serving.

However, these are ideal future assumptions; today several issues remain, such as significantly higher end‑to‑end latency compared to pure real‑time pipelines, which depends on checkpoint intervals.

As these frameworks continue to evolve, the future may look different.

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