Using Doris for Real‑Time Data Warehousing: Benefits, Drawbacks, and Comparison with Flink

Recent advances in Doris, such as hot‑cold separation, have significantly improved its usability and cost efficiency, making it a popular choice for storage‑centric real‑time data warehouses.

The author explains that many organizations adopt Doris for real‑time analytics because developing Flink jobs is considerably more complex, especially for large windows, stateful processing, and frequent dimension table changes; Doris lowers the development barrier.

Compared with Flink, Doris‑based solutions offer lower entry thresholds, simpler operations, faster development cycles, and easier data debugging since all intermediate results are persisted in tables.

However, the approach also has notable drawbacks: latency is higher because Doris jobs are typically scheduled at intervals of 30 seconds or more, making it unsuitable for ultra‑low‑latency metrics; and the system struggles with very high TPS or massive single‑scan data volumes, performing best under tens of millions of rows.

When deploying Doris as the core of a real‑time pipeline, teams must enforce strict tooling for alerts, job monitoring, lineage, and resource management, as any bottlenecks in SQL performance or resource allocation can affect all dependent tasks.