Why Paimon + StarRocks Is the New Real‑Time Lakehouse Choice for Big Tech

Background and Motivation

Over the past decade, data architectures have evolved from traditional data warehouses (Teradata, Oracle) to Hadoop/Hive‑based big‑data platforms, and now to integrated lakehouse solutions. Large enterprises face increasing pressure for real‑time data freshness, low storage cost, and unified analytics.

Why Large Companies Choose This Stack

Technical selections in big tech are driven by concrete pain points rather than hype:

Inconsistent data metrics between offline and streaming pipelines.

Exploding storage costs caused by duplicated copies in Kafka, HBase, and offline warehouses.

High maintenance overhead due to separate development and operations teams for batch and streaming.

These challenges demand a unified architecture that stores data once, computes it once, and provides instant visibility.

Technical Deep Dive: Paimon

Paimon, one of the three “lakehouse swords” (Iceberg, Hudi, Paimon), distinguishes itself with native streaming support and an LSM‑Tree storage layout. Its upsert capability allows primary‑key updates directly, avoiding the costly Merge‑on‑Read patterns required by Iceberg and Hudi. Tight integration with Flink CDC lets it ingest change‑data streams with minute‑level latency, and its data can be persisted to low‑cost object storage (OSS/S3).

Technical Deep Dive: StarRocks

StarRocks addresses the OLAP side. Unlike ClickHouse, which excels at append‑only workloads but lacks transactional updates and suffers on complex joins, StarRocks uses a fully vectorized C++ engine and a cost‑based optimizer (CBO) to achieve 3‑5× faster query performance on multi‑table joins. Its materialized view feature automatically pre‑aggregates detail data, eliminating manual ETL. Moreover, StarRocks can query Paimon tables via an external catalog, enabling a “one‑store, two‑capabilities” model.

Combined Architecture in Practice

Typical deployment in a large‑scale environment looks like:

ODS/DWD layer (raw & detailed data): Paimon is chosen for its columnar storage, low‑cost object storage, and native upsert support, handling high‑frequency data changes.

DWS/ADS layer (service & application): StarRocks serves BI dashboards, real‑time dashboards, and ad‑hoc queries, leveraging its high concurrency and low latency.

Integration: StarRocks maps Paimon tables through an external catalog or materialized views, achieving hot‑cold data separation and eliminating data movement.

Comparison with Alternatives

Key capability differences are summarized below:

Write & Update: Hudi/Iceberg – weak upsert, small‑file issues; ClickHouse – fast append only, no primary‑key updates; Paimon+StarRocks – strong native upsert and streaming writes.

Query Performance: Hudi/Iceberg + Trino – moderate, limited by file formats; ClickHouse – fast but weak on multi‑table joins; StarRocks – vectorized engine + CBO, excels on complex joins.

Storage Cost: Hudi/Iceberg – low (object storage); ClickHouse – high (SSD); Paimon+StarRocks – low (object storage for Paimon, optional compute‑separate deployment for StarRocks).

Operational Complexity: Hudi/Iceberg – high (multiple pipelines); ClickHouse – medium (manual materialized view design); Paimon+StarRocks – simple (SQL‑only interface, automated materialized views).

Typical Scenarios: Hudi/Iceberg – offline warehousing, shared data lake; ClickHouse – high‑concurrency point queries, simple aggregates; Paimon+StarRocks – real‑time warehousing, complex ad‑hoc analysis, frequent master‑data updates.

Conclusion

If a business requires real‑time data updates together with high‑performance, complex analytical queries, the Paimon + StarRocks combination currently offers the most balanced solution in terms of cost, performance, and operational simplicity. It reflects the industry shift toward lakehouse architectures that meet the growing demand for immediate data visibility.