This article explores the integration of Flink, Airflow, and StarRocks for real-time data processing. It details different methods for data ingestion, including routine loads and Kafka connectors, while sharing lessons learned from implementation. The author concludes with a preference for the Flink Connector due to its flexibility and existing infrastructure.

This article discusses the upcoming incremental view maintenance (IVM) feature in StarRocks, focusing on its architecture and advantages over previous methods. It explains how IVM allows for efficient updates to materialized views by only processing changed data, thus improving performance and reducing computational overhead.

The article delves into the complexities of StarRocks' implementation of Iceberg's Merge-on-Read (MoR) functionality, specifically focusing on how it efficiently manages deletes with positional and equality delete files. It explores the intricacies of query planning, the role of queue structures in processing, and the handling of schema evolution, all while shedding light on the technical challenges encountered during the exploration of the system's codebase.