Why Apache Paimon Is Revolutionizing Streaming Lakehouse Architecture with Flink

Data Analysis Architecture Evolution

Data analysis architectures are moving from traditional Hive/Hadoop warehouses toward lakehouse solutions such as Presto, Spark, OSS, and lake formats like Delta, Hudi, Iceberg. OSS offers elastic, compute‑storage separation and hot‑cold storage, while lake formats provide ACID, time‑travel, schema evolution, and faster query planning.

Many companies still retain Hive because the new benefits are not always essential. Upgrading to a lakehouse can improve timeliness, allowing selective real‑time updates while keeping most data offline.

Introducing Apache Paimon

Apache Paimon is a streaming‑first lake format born from the Flink community. It integrates tightly with Flink CDC to support schema evolution and full‑database synchronization, and can also be accessed via Spark, Hive, Trino, StarRocks, etc.

Compared with Iceberg and Hudi, which are batch‑oriented and Spark‑centric, Paimon is designed for continuous updates and native changelog handling.

Flink + Paimon Streaming Lakehouse

By combining Flink with Paimon, a streaming lakehouse can replace Hive partition tables with primary‑key tables, providing real‑time visibility, tag‑based snapshots for consistent reads, and low‑cost storage through file reuse.

Key mechanisms include a consumer‑style snapshot retention to avoid FileNotFoundException and a changelog producer that generates accurate update streams for downstream processing.

Performance Benchmarks

In Alibaba Cloud tests, Paimon achieved up to 4× higher ingestion throughput and 10‑20× faster query performance than Hudi for 500 million rows, and 12× better merge‑on‑write performance for 100 million rows.

Demo

A real‑time e‑commerce analytics demo shows data flowing from ODS to DWD, DWM, and DWS using Flink and Paimon, illustrating the end‑to‑end streaming lakehouse workflow.