Key Frameworks and Characteristics of Lakehouse Architecture: A Ground‑Level Perspective

One Basic Observation: Lakehouse Architecture Comes in Multiple Forms

Many online articles describe implementations based on Doris, Paimon + Flink, and other technology stacks.

Examples include:

"From ClickHouse to Doris: Lakehouse Architecture Upgrade Practice"

"ByteDance's Lakehouse Solution Based on Apache Hudi"

"Bilibili's Lakehouse Architecture Practice Based on Iceberg"

Thus, implementations based on Doris, Hudi, Iceberg, etc., differ across companies, but a common consensus is that lake storage frameworks (Hudi/Iceberg/Paimon), OLAP engines (ClickHouse/Doris), and compute engines (Flink/Spark) all play crucial roles.

Key Features and Frameworks of Lakehouse Architecture

Storage‑compute separation is the prevailing design.

Current storage‑side frameworks like Hudi or Paimon offer the lowest overall cost and highest efficiency, making them the most recommended choices.

Moreover, communities prioritize compatibility between Flink, Doris, and lake storage frameworks; for example, Doris/StarRocks already support Hudi and Paimon capabilities.

Read‑write capabilities are increasingly demanding.

Compared with traditional data‑warehouse development, lakehouse architectures impose stricter requirements on read/write performance and ACID guarantees.

This shift is familiar to many, as the classic Lambda architecture remains the mainstream in many enterprises.

The traditional model is constrained by storage and compute costs as well as limited read/write throughput.

Consequently, frameworks such as Paimon and Doris have heavily optimized their read/write paths—Paimon adds primary‑key support with extensive optimizations, while Doris, as an OLAP engine, also performs substantial read/write tuning.

Additional concerns like data governance and data services exist but are not elaborated here.

Internal Adoption Preferences

In larger companies, after small‑scale validation, there is a more aggressive push to merge offline and online processing, prioritizing large‑scale rollout alongside framework capabilities.

In smaller firms or niche scenarios, the specific features of these frameworks—such as changelog handling or partial updates—are the primary focus.

Future community sharing will likely fall into two categories: macro‑level narratives focusing on cost and architectural evolution, and technical‑solution narratives targeting specific business scenarios.

Both categories are valuable; they simply reflect different perspectives.

Therefore, we should monitor both high‑level architectural trends and concrete implementation details.

The writing is a bit chaotic—please read it as a rough sketch. 😄